Use of PLEXOS to Assess the Operational And Costs Effects of Grid Integration, Ancillary Services and Balancing Dr Christos Papadopoulos Regional Manager.

Use of PLEXOS to Assess the Operational And Costs Effects of Grid Integration, Ancillary Services and Balancing Dr Christos Papadopoulos Regional Manager - Europe Energy Exemplar (Europe) Ltd 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop - Berlin 2012

3rd European Electricity Ancillary Services & Balancing Forum Workshop
Energy Exemplar® Commercial since 1999 Focused on PLEXOS® for Power Systems software Global client base served from three locations: Adelaide, Australia London, UK California, USA 20% staff with Ph.D. level qualifications spanning Operations Research, Electrical Engineering, Economics, Mathematics and Statistics Client base growing >20% p.a. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Overview of Features and Uses
5/11/2011 PLEXOS® Modelling Tour PLEXOS Overview of Features and Uses 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® for Power Systems – Market Simulation & Analysis
Proven power market simulation tool Uses mathematical programming, optimisation and stochastic techniques Robust analytical framework, used by: Energy Producers, Traders and Retailers Transmission System /Market Operators Energy Regulators/Commissions Consultants, Analysts and Research Institutions Power Plant Manufacturers and Construction companies Power system model scalable to thousands of generators and transmission lines and nodes 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Scalability System size: From single generator to 1000’s From single transmission node to 10000’s Largest system studied: Eastern Interconnect (US) nodes 7000+ generators Simulation interval: Switch easily between hourly, half-hourly and 1-minute (or any other timeframe) with a simple option 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

What can be achieved with PLEXOS®
Power Market Modelling, Simulation and Analysis - short & long term: Price Forecasts based on power system operational constraints and market fundamentals, at nodal and regional level. Detailed operational planning and dispatch optimization while modelling complex renewable-hydro-thermal and transmission Renewable integration analysis Investment planning and analysis Optimise new generation and transmission builds and retirements – what, when, where? Assessing the effectiveness of investment decisions and policies Portfolio optimization and valuation 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

What can be Achieved with PLEXOS® (2)
Risk management via scenario analysis, stochastic modelling and optimization: Optimal resource allocation decisions (fuel, heat, capacity) over the long or short term subject to uncertainty (e.g. volatility in fuel prices, wind, hydro inflows, demand) Fuel, Emissions and hedge contract evaluation and analysis Transmission and Ancillary Services/Balancing Analysis Regional, Zonal or Nodal Congestion Forecast and Management Security Constrained Dispatch (N-x) Co-optimization of ancillary services/reserve and energy dispatch Optimal power flow modelling Interconnector Modelling 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Gas Modelling (2012)
18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Electricity and Gas Modelling
Goal is to provide modellers an integrated gas and electricity model that is straight-forward for electric market modellers to understand and use. Initially the details of pipeline pressures and pressure drop functions not modelled. Storage volumes, pipeline flows and gas demands can be expressed in potential energy terms. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Electricity and Gas Modelling (2)
Short and long term simulations Both system-planner (cost minimisation) and strategic (maximise profit) solutions Investment planning: Gas field, storage, and pipeline potential candidates defined with: Capital cost of construction (builds cost, WACC, economic life, project start date, min/max build constraints) Operating costs (fixed and variable) Co-optimisation of both Electricity and Gas Markets!! 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Features: Overview Generation Optimal capacity expansion Unit commitment Heat rate model Maintenance optimization Monte Carlo simulation Fuel constraints Emission constraints Technical limits Auxiliary use Ancillary services CCGT & CHP SCUC (Contingency) Transmission Radial and meshed networks Regional pricing Nodal pricing Large-scale networks Interface limits Losses LMP decomposition Wheeling charges Pricing methods Contingencies and SC-OPF SCUC (Contingency) ISO level outputs Renewables All types Energy constraints Must-run limits External profiles Cascading Hydro Pumped Storage Uncertainty Markets/Portfolio Optimisation Energy Ancillary Services Heat Fuel Capacity Integrated Gas/Fuel Modelling Field Storage Pipeline Node Financials Financial contracts CfD & FTR Generator bid formation Gaming models Pricing and Uplift Escalators Stochastics Variable inputs Correlations Stochastic optimization Monte Carlo Timeframes 5-minutes to 10’s of years Constraint decomposition LDC model Chronological model Time Slices Visualisation Geospatial user requested results graphs Google Earth Customizations Generic constraints Programming API Automation Data retrieval Demand Bidding/Participation Energy Ancillary Services 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Simulation and Analysis tools in PLEXOS – Seamless Integration
LT Plan – Long Term Optimal Investment PASA – Optimal Maintenance Scheduling MT Schedule – Medium Term Decomposition ST Schedule – Short Term Chronological 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

LT Plan - Long Term Capacity Expansion Planning
Finds the optimal combination of generation and transmission new builds and retirements that minimizes the net present value of the total costs (incl. fixed and variable operating costs) of the system over a long-term planning horizon. The following types of expansion/retirements and features are supported: Building and retiring generating plants and transmission lines Multi-stage build projects Expanding the capacity on existing transmission interfaces Taking up new physical load /generation contracts Deterministic or stochastic optimization 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PASA - Projected Assessment of System Adequacy
PASA is a simulation that focuses on the balance of supply and demand in the medium term. When used in combination with MT Schedule and/or ST Schedule, the primary purpose of the PASA is to determine, where and when maintenance outages should occur. It can model planned and random outages of generation plants and transmission lines, and its severity In multi-region models PASA calculates the optimal amount of reserve that should be shared between regions using the transmission network. (Equalizing regional capacity reserves done using quadratic programming formulation.) 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

MT Schedule - Medium Term Scheduling and Simulation
MT Schedule is used to give fast results for medium to long-term studies. The MT Schedule handles all user-defined constraints including those that span several weeks, months, or years. This might include: Fuel off-take commitments e.g. gas take-or-pay contracts Energy limits, Emission quotas Long-term storage management taking into account inflow uncertainty MT Schedule: Gives the option of Load Duration Curves or Chronological modelling approach, similar to that in LT Plan. Each constraint is optimised over its original timeframe and the MT Schedule to ST Schedule Bridge algorithm converts the solution obtained, e.g. a storage trajectory, to targets or allocations for use in the shorter step of ST Schedule Can model competitive behaviour of portfolios over the medium term. (Sophisticated game-theoretic behaviours like Nash-Cournot competition or ‘simply’ recovery of fixed costs.) 3rd European Electricity Ancillary Services & Balancing Forum Workshop 18/09/2018

ST Schedule ST Schedule is mixed-integer programming (MIP) based chronological optimization. It can emulate the dispatch and pricing of real market-clearing engines, but it provides a wealth of additional functionality to deal with: unit commitment; constraint modelling; financial/portfolio optimization; and Monte Carlo simulation. ST Schedule provides two methods for modelling the chronology: Full Chronology Every trading period (interval) inside the ST Schedule horizon is modelled explicitly. (Interval can be 1min to 24hrs in length.) Typical Week One week is modelled each per month in the horizon and results are applied to the other weeks. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Mechanism for Calculating Market Price
The market price of energy is the marginal cost (as represented by generator offers) of serving consumption at each node or region The marginal cost is found by simulating the least-cost economic dispatch of the entire market, emulating the steps followed by a Market Operator, subject to all: Generation technical characteristics and constraints; Transmission technical characteristics and constraints; and Forecast of load/demand and renewable generation The market price is made up of the marginal cost of: Generation; Transmission losses, to that node; and Transmission congestion, to that node PLEXOS therefore can fully replicate the Nodal or Locational Marginal Pricing (LMP) market rules. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Modelling of All Renewable Energy Sources
Hydro/Pump Wind Solar (PV, CSP) Geothermal Bio-energy Marine + others 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Features: Thermal Unit Commitment
Highly detailed thermal unit commitment model: Polynomial heat rate functions Minimum up and down time constraints Energy ramping constraints Starts costs variable by cooling state Run up and down periods Rough-running zones Combined-cycle model: Genuine optimisation of CCGT operation Models of GT and steam units Combined heat and power 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Features: Emissions and Fuels
Scope: Any number and types of fuels and emissions across any set of Generators Fuel Contracts Optimal capacity expansion, dispatch and pricing reflect all constraints Constraints and pricing: Minimums (take-or-pay) and maximum constraints Emission limits across any timeframe including multi-annual constraints Fuel constraints across any timeframe (interval, day, week, month, year, multi-annual) Fuel and emission shadow and accounting prices 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Features: Hydro Multiple phase simulation (LT>MT>ST): Long-term storage decomposition via targets or water values to shorter-term (more detailed) phases Ensures optimal use of storage down to chronological level Cascading networks: Major and minor storages and junctions Natural inflows and spillways and canals Constraints: Minimum releases for environment Operational constraints and hydro generation efficiency functions Monte Carlo or Stochastic optimisation: Any number of iterations with variation in any input 2-stage stochastic optimisation for better modelling of storage release policies under uncertainty Applications worldwide 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

e.g.: Chilean Hydro System

Features: Pumped Storage
Pumped storage modelled as physical system with upper and lower storages Genuine optimisation of generation and pumping operations 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Comparison – Pump operation and price
Pump storage unit pump during low price periods and generates at high prices periods as required. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS and Policy Analysis
PLEXOS has a proven track record in the area of policy analysis and development. Common policy analysis with PLEXOS includes: The design, analysis, and benchmarking of electricity market rules and effect on market participants. Assessing the effectiveness of renewable technology policies and resulting impact on carbon emissions, prices, transmission grid operations and investment incentives. Forecasting market entry and assessing future technology and fuel mixes as well as examining the development of system adequacy. Examining market competitiveness and market power. Evaluating generation or transmission investments 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour Part 1 - Modelling of Ancillary Services Products and Requirements with PLEXOS Dr Christos Papadopoulos Regional Manager - Europe Energy Exemplar (Europe) Ltd 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour
Optimisation of Ancillary Services Integrated with Unit Commitment and Transmission Model 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Services Products More and more the last decade, it has been recognised that AS and Energy markets are closely coupled as the same resource and same capacity have to be used to provide multiple products when justified by economics. The capacity coupling for the provision of Energy and AS, calls for joint optimisation of Energy and AS markets that differs from market to market due to different regional reliability standards and operational practises. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Service Products (cont) Reliable and Secure System Operation requires the following product and Services (not exhausted): Energy Regulation & Load Following Services – AGC/Real time maintenance of system’s phase angle and balancing of supply/demand variations. Synchronised Reserve – 10 min Spinning up and down Non-Synchronised Reserve – 10 min up and down Operating Reserve – 30 min response time Voltage Support – RPS, Locational Specific Black Start – (Service Contracts) 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Energy & Ancillary Services Co-optimisation
In general three basic approaches: Independent Merit Order Dispatch - Leads to infeasibilities Sequential Market Clearing - Ignores interdependencies Joint Optimisation - Price equity for both energy and AS services products provision... 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Energy & Ancillary Services Co-optimisation (cont)
Mathematical optimisation of energy and ancillary services dispatch and pricing, integrated with unit commitment and transmission model. Complete flexibility in defining Requirements across areas/generators Dynamic risks based on load, generation, transmission flows. Combined energy/reserve ramping limits Constraints on provision during unit run up and down periods Regulation (AGC) range constraints Interruptible loads Pumping load as reserve Energy usage for hydros 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Solving SC UC/ED using MIP Unit Commitment and Economical Dispatch can be formulated as a linear problem (after linearization) with integer variables of generator on-line status Minimize Cost = generator fuel + VOM cost + generator start cost + contract purchase cost – contract sale saving + transmission wheeling + energy / AS / fuel / capacity market purchase cost – energy / AS / fuel / capacity market sale revenue Subject to: Energy balance constraints Operation reserve constraints Generator and contract chronological constraints: ramp, min up/down, min capacity, etc. Generator and contract energy limits: hourly / daily / weekly / … Transmission limits Fuel limits: pipeline, daily / weekly/ … Emission limits: daily / weekly / … Others 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Formulation of Energy/AS Co-optimization

Ancillary services modelling In PLEXOS involves: Nature of ancillary service: regulation up, regulation down, spinning, non-spinning, etc. Ancillary service requirements in MW: % of Load, Minimum requirement, contingencies, etc. Ancillary service providers: Generators, Ancillary Service Markets, Demand Side Program, etc. Provision limit in MW: Max provision, Ramp Rate, etc. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Nature of Ancillary Service:
Properties of Reserve Class define the nature of the ancillary service: Min Raise: Spinning reserve – on-line resources can provide service Min Lower: Spinning down reserve – on-line resources can provide service Min Regulation Raise: Regulation up reserve – on-line resources can provide service Min Regulation Lower: Regulation down reserve – on-line resources can provide service Min Replacement: Non-spinning reserve – off-line resources can provide service Min Operational: Non-spinning reserve – on-line resources and off-line quick start can provide service Reserve [Mutually Exclusive]: when this flag is set to true it means that two Reserves of the same type e.g. both “spin up” cannot share the same spare capacity Only one of the properties can be specified for each Reserve object 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Service Requirements
Ancillary service requirements can be specified as Min Requirement, …, Biggest Generator contingency as Percentage of regional demand as The final requirement is the maximum of the three and scaled down by Reserve [Risk Adjustment Factor] + Static Risk Parent Class Child Class Collection Reserve Generator Generator Contingencies AS1 – Spinning Reserve Coal_Gen Gas_Gen Gas_Gen_2 Hydro Reserve Region Property Value Units Band AS1 – Spinning Reserve Demo Demand Risk 7 % 1 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Service Requirements
Ancillary service requirements are hard constraints by default Define Reserve [VoRS] (Value of Reserve Shortfall) to specify ancillary services as soft constraints, the violation will pay at the price of VoRS Coordination of energy requirement and AS requirement constraints Region [VoLL] (Value of Loss of Load) > Reserve [VoRS], AS requirement will be violated before energy requirement if there is capacity shortage Region [VoLL] < Reserve [VoRS], energy requirement will be violated before AS requirement if there is capacity shortage Reserve [Max Provision] will limit the total AS provision 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Service Providers
Ancillary service can be provided by Generators, Energy Purchaser (distributors) Ancillary service provision limits for on-line AS {Max Capacity (or rating) – Generation} for upward AS {Generation – Min Stable Level} for downward AS User-specified Reserve [Generators] [Max Response] If Reserve [Timeframe] is defined, Ramp Rate * Timeframe The final provision limit if the minimum of the three The final provision limit can be scaled by Reserve [Generators] [Effectiveness] Ancillary service provision limits for off-line AS Minimum of Generator [Max Capacity] or Generator [Rating] and Reserve [Generators] [Max Replacement] 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Modelling for Renewable Integration and Ancillary Services Requirements 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Coping with Uncertainty
PLEXOS offers three distinct approaches to coping with uncertainty: Scenario analysis Monte Carlo simulation Stochastic optimisation In PLEXOS any parameter can easily have uncertainty applied to it. Common parameters to undertake analysis of include: Load growth and load shapes Fuel and emission prices Renewable uptake and energy production Technology cost trends 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Modelling Any input parameter can be stochastic: E.g. Wind, Inflows, Fuel prices, Load. User defined stochastic samples Generated by external program and read into PLEXOS E.g. wind forecasts PLEXOS generated stochastic samples Autocorrelation model Brownian Motion with Mean Reversion Box-Jenkins methods (ARMA, ARIMA) 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Modelling - Monte Carlo vs. Optimisation
Monte Carlo simulation (Parallel Option) assumes perfect foresight for each stochastic sample PLEXOS then computes the optimal decision for each of a number of possible stochastic samples independently Stochastic Optimisation Simultaneously considers all the possible stochastic samples and associated probabilities PLEXOS computes a single optimal decision that is best hedged for the uncertainty represented by the stochastic samples 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Modelling – Monte Carlo

Stochastic Modelling – Stochastic Optimisation

Value of water and uncertainty
Stochastic Optimization “Short-sighted” solution Looking ahead Leaving for “tomorrow” what is needed More conservative releasing approach 1) Independent Samples 3) Stochastic Solution with enough foresight Again, it is draining the storage at the end of the horizon (full resource usage) Looks like an average, but it is not! It is the optimal level 2) Stochastic Solution 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Modelling To model input values as stochastic drivers Define stochastic Variables in Variable class Specify stochastic characteristics Specify number of iterations for stochastic sampling and simulation Properties of the Stochastic object Assign Variables to stochastic drivers Need to decide period of stochastic change Every Minute, Hourly, Daily, Weekly, Monthly, Annually 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Define Stochastic Variables
Define a Variable object in Variable class Define stochastic characteristics Two methods to define stochastic Variables Exogenous sampling: user-defined profile samples (with assigned probability for each sample) Endogenous sampling: user-defined expected profile that will be scaled up and down by random samples with random numbers with user-specified distribution 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Exogenous Sampling User-defined profile samples (with assigned probability for each sample) Property Value Units Band Data File Random Number Seed 2 - 1 Sampling Method Profile 10 Stochastic Gas Forward Price.csv Year Month Day Period 1 2 3 4 5 6 7 8 9 10 2005 6.45 7.32 6.96 6.58 8.19 7.25 7.15 7.38 7.08 7.93 6.04 5.86 7.43 6.02 7.14 6.55 6.8 7.79 6.17 6.27 6.37 6.85 8.21 6.65 6.78 5.94 8.45 7.72 6.09 6.05 8.3 5.78 6.08 7.99 7.05 6.34 7.28 6.12 6.46 8.39 8.25 6.91 7.22 5.89 6.79 7.53 8.15 … 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Endogenous Sampling User-defined expected profile that will be scaled up and down by random number samples with user-specified distribution Normal distribution Expected profile Property Value Units Band Data File Scenario Random Number Seed 3 - 1 Stochastic runs Sampling Method 2 Distribution Type Profile Wind Generation Profile Error Std Dev 50 % Min Value Max Value Mean Reversion 0.2 Auto Correlation 80 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Runs and Monte Carlo
5/11/2011 Stochastic Runs and Monte Carlo Number of samples for each Variable object: Exogenous: Number of randomly selected sequences (in bands) Endogenous: Number of sequence drawn for each variable Number randomly generated Forced Outages. Forced Outages: Tells PLEXOS if random outages should be drawn for all phases Maintenance: Tells PLEXOS if maintenance outage should be generated by PASA. Monte Carlo: Force Outages frequency are distributed according to a Weibull pdf. Convergent Monte Carlo: ‘Winning’ Outage Pattern after Chi-square test The total number of independent samples executions or scenarios for each SO run is always the number of Stochastic Samples if Variable objects are defined. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Optimization (SO)
Fix perfect foresight issue Monte Carlo simulation can tell us what the optimal decision is for each of a number of possible outcomes assuming perfect foresight for each scenario independently; It cannot answer the question: what decision should I make now given the uncertainty in the inputs? Stochastic Programming The goal of SO is to find some policy that is feasible for all (or almost all) the possible data instances and maximize the expectation of some function of the decisions and the random variables PLEXOS uses scenario-wise decomposition 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Variables Set of uncertain inputs ω can contain any property that can be made variable in PLEXOS: Load Fuel prices Electric prices Ancillary services prices Hydro inflows Wind energy, etc Number of samples S limited only by computing memory First-stage variables depend on the simulation phase Remainder of the formulation is repeated S times 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour Stochastic Unit Commitment 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

SO in Unit Commitment Stochastic Unit Commitment for System Operator: On/off decisions for thermal units Minimise total system cost SO can provide more robust DA schedule Price-based Unit Commitment (PBUC): On/off decisions for thermal Use of contracts Maximise profit SO can increase return on investment and help manage constraints such as fuel budgets 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

SO in Unit Commitment (cont)
Assumption is that we must make certain commitment decisions ‘now’; and cannot perfectly anticipate certain variables such as load or wind dispatch. Simulating a number of independent samples can give ambiguous results because each sample has perfect foresight: units on in some samples and off in others; or starting and stopping at different times Thus we wish to find the optimal on/off decisions for selected generating units over a given “non-anticipativity window” given our knowledge of the uncertainty 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour Stochastic Unit Commitment for System Operator 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day-ahead Unit Commitment Example
CAPACITY TECHNICAL LIMITATIONS MINIMUM PRODUCTION PRODUCTION COST 2x100 [MW] -12hrs off -8hrs on [65] MW 10$/MWh 100 [MW] -4hrs on -2hrs off [10] MW 50$/MWh 0-100 [MW] uncertain Must-run! - 0$/MWh How to efficiently schedule thermal power plants with technical restrictions if we don’t know how much wind (and/or load) is going to be available? 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day-ahead Unit Commitment, Continued
No wind generation is available Assume for example a worst-case scenario analysis. First, the wind is absent during the entire day (pessimistic) Two base load “slow” units can be scheduled Fast units are required just in order to meet the load 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Now assume an optimistic scenario analysis. Wind is going to be available during the entire day High wind resources Fast units in order to avoid unserved energy One base load “slow” unit pre-schedule The question is: If we don’t know how the wind is going to be… what to do? Dispatch one or two slow base units? 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Stochastic Optimisation: Two stage scenario-wise decomposition Take the optimal decision 2 Expected cost of decisions 1+2 Is there a better Decision 1? Take Decision 1 Reveal the many possible outcomes Stage 1: Commit 1 or 2 or none of the “slow” generators Stage 2: There are hundreds of possible wind speeds. For each wind profile, decide the optimal commitment of the other units and dispatch of all units RESULT: Optimal unit commitment for “slow” generator 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

In conclusion: SO solution provides a more conservative and robust unit commitment solution for the day-ahead market Because SO is embedded at the deepest level of the optimisation the outcomes respect all input constraints including security-constrained transmission, fuel, emissions, etc, so one expects the SO-based DA schedule to yield in real-time dispatch: Less congestion Less use of emergency generation/load management solutions More conservative/robust ancillary services dispatch 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary services modelling - Example At different resolutions Dynamic requirements With stochastic optimisation 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Model topology Simple 3 node demonstration system Mix of generation types Variable wind generation (no possible curtailment) Hourly load profile 15-minute despatch resolution 3 1 2 Load 2000MW 800MW CCGT 1 CCGT 2 Oil Coal Plant 1 Coal Plant 2 Hydro Wind 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Base model generation result Peaking plant in orange operating at first spike Some displacement of hydro to allow for ramping Variable wind in green 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Spinning Reserve Requirement CCGT now runs all day to cover reserves and energy Coal plant 2 also online longer Oil unit not required Less displacement of hydro generation for ramping 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Spinning reserve provision Reserve provision changes between units In general it is better for hydro to provide energy even though it has the ability to provide a lot of spinning reserves 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Higher resolution despatch – 5 Minute modelling Oil unit required at peak for increased variability Increased displacement of base load to cover for ramping constraints 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Higher resolution despatch – 5 Minute modelling When dispatched at more detailed resolution, ramping constraints become more important More expensive solution required due to binding ramping constraints Ramping constraints will also bind on reserve provision 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Contingency Analysis – Hydro unit contingency Dynamic changes to reserve requirements Lower requirements leave more room for energy provision 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Energy/AS Stochastic Co-optimisation!!! So far the model example has had perfect information on future wind and load requirements. Uncertainty in our model inputs should affect our decisions – Stochastic optimisation (SO) The goal of SO then is to find some policy that is feasible for all (or almost all) the possible data instances and maximise the expectation of some function of the decisions and the random variables What decision should I make now given the uncertainty in the inputs? 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Energy/AS Stochastic Co-optimisation (cont) Even though load lower (wind unchanged) more units must be committed to cover the possibility of high load and low wind These units must then operate at or above MSL 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Energy/AS Stochastic Co-optimisation (cont) Oil unit not required in sample 1 in first hour but is by sample 2 – unit must therefore be online at MSL or above for all samples More conservative / expensive despatch required. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Ancillary Service Pricing
If Reserve [Generators] [Offer Quantity] and [Offer Price] are not defined, AS requirement is not binding, zero price is reported for the ancillary service (shadow) price AS requirement is binding, the shadow price of the AS requirement (or Reserve [VoRS] if it is defined) is reported If Reserve [Generators] [Offer Quantity] and [Offer Price] are defined, AS requirement is not binding, the last cleared AS Offer Price is reported the ancillary service (shadow) price 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Reserve Pricing Generator.Reserve.Offer Quantity/Price
Reserve Requirement is binding constraint Reserve Price No Zero price Yes Generator.Reserve.Offer Price Reserve Shadow Price (opportunity cost) Max of Generator.Reserve.Offer Price and Reserve Shadow Price 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Offers for reserve provision - AS Pricing The Energy and AS co-optimization results in an endogenous 'price' for the provision of AS by each generator. There will naturally be one or more marginal generators for energy and one or more for each ancillary service, and the market prices for the ancillary services will naturally emerge from the optimization. In some markets though, generators and loads can place offer prices on provision of reserves. PLEXOS provides the properties Reserve [Generators] [Offer Price], [Offer Quantity] for provision of spinning and regulation and [Sync Cond Offer Price] for provision of spinning reserve from synchronous condenser mode, and finally [Pump Offer Price] for the provision of pump interruptible load. These offer prices add to the endogenous price of reserve provision, thus they act as a premium on the service. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS provides a comprehensive range of output data from the Energy/AS Co-optimization: How much reserve is required in each simulation period (Risk). What price and hence cost was imposed by AS (Price, Cost) Which generators and / or interruptible load provide reserves: Provision, and Provision. How AS costs were allocated to generators (Cost) What AS revenues and cost flowed back to companies (Company Net Reserves Revenue). 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour Part 2 - Modelling and Analysis of Grid Integration and Balancing Markets. Dr Christos Papadopoulos Regional Manager - Europe Energy Exemplar (Europe) Ltd 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Modelling Grid - Interconnections
PLEXOS® Modelling Tour Modelling Grid - Interconnections 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Transmission Network Modelling
Two kinds of power flow can be modelled Transportation model Power flows as directed flows Transmission network (Kirchhoff's law must be obeyed) More power flow in lower reactance transmission lines Direct-Current Optimal Power Flow (DC-OPF) method is used to solve the power flow in the transmission network 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

DC - OPF Methods Optimal power flow (OPF) Refers to the generator dispatch and resulting AC power flows that is minimum cost and feasible with respect to thermal limits on the AC transmission lines. The OPF might include other constraints such as interface limits, and other decisions such as the optimal flow on DC lines and phase shifter angles. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS - Linearised DC-OPF The OPF using a linearisation of the power flow equations which considers only real power flows and assumes voltages are all 1 p.u. It is important not to confuse Linearised DC-OPF with a transportation solution. In a transportation model the flow on all lines is controllable, but in a DC-OPF the KVL constraints are applied so flows mimic AC flows. PLEXOS provides two DC-OPF formulations: set by the option Transmission [OPF Method]: 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Fixed Shift Factor OPF Network shift-factors are pre-computed and used to create “side-constraints” to enforce transmission constraints and model transmission losses. Variable Shift Factor OPF Bus (node) phase angles and branch (line) flows are decision variables in the optimisation, thus the shift-factors are implicit and no pre-computation is required, but the formulation size is potentially very large. These options represent the two most commonly used DC-OPF formulations with some unique and powerful enhancements in modelling losses and security-constrained optimal power flow. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Interconnector modelling
Generally market interconnectors are modelled as simple DC lines (transportation model) with thermal limits. Available Transfer Capacity (ATC) modelling Simple DC lines with max flow set at ATC values Flow Based (FB) modelling allows for more flexible use of interconnectors. Use rules published by TSO to either: Create custom constraint on line flows Use interface objects with flow coefficients and RHS values 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS - Security-Constrained Unit Commitment (SCUC) Security constrained unit commitment (SCUC) finds the optimal unit commitment and dispatch solution subject to the transmission being feasible if any defined contingency (such as the loss of a line or generator) should occur. The SCUC algorithm in PLEXOS computes contingency shift factors - CSFs (also called generation-shift sensitivity factors) which define how much of the flow lost during a contingency will appear on other lines in the network: these factors are used to monitor and enforce the contingency constraints. The resulting dispatch is more conservative and at higher cost, but reflects more accurately the actual system operation. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Grid Congestion & Pricing A nodal price represents the cost to the system as a whole of a unit change in load at the bus. In the absence of either constraints or losses all nodal prices will be equal. This uniform price is referred to as the system lambda or network energy charge. No matter where we perturb load in the network, the marginal impact on total system cost would be the same. As we introduce constraints on the transmission flows (either on individual branches or combinations of flows), the nodal prices diverge. Congestions and Losses are reflected by the separation/differences of nodal (LM) prices across the network. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Interconnector modelling - Example
Compare modelling interconnectors on ATC or FB basis: ATC: Two interconnectors with thermal limit 300MW in winter and 400MW in summer FB: Two interconnectors with sum of flow limited to 600MW winter and 800MW summer. Flow on each interconnector limited 450MW winter and 600MW summer. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Comparison – Imports and exports
Greater flexibility of FB allows both greater export and import of energy. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Comparison – Prices Flow based rules result in lower prices on average. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Interconnector modelling - Expansion
PLEXOS can be used to optimise the expansion of transmission lines. For this exercise we simply examine the results of increasing the CH-DE interconnector capacity. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Comparison – Imports and exports
Greater interconnector capacity increases both imports and exports 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Comparison – Average monthly prices
Greater interconnector capacity reduces price – greater price convergence 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day Ahead & Balancing Markets
PLEXOS® Modelling Tour Day Ahead & Balancing Markets 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Balancing market modelling
PLEXOS is used by our clients to examine the balancing market operation including: Forecasting balancing costs Evaluating transmission investments against balancing improvements Optimising / evaluating balancing measures and contracts 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day Ahead & Real Time modelling
Balancing market modelling is generally undertaken in two stages: Stage 1 – Unit commitment is made on forecast of load and renewable generation. In the UK market transmission constraints are not taken into account Stage 2 Balancing – PLEXOS optimises the balancing of units to meet actual load and renewable generation while obeying transmission constraints. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day Ahead – Real Time modelling (cont)
5/11/2011 Day Ahead – Real Time modelling (cont) In a balancing (or real-time RT) market, generators define their dispatch level from a Day-ahead (DA) market using the properties Generator Offer Base (for Generation) and Pump Bid Base (for Pump Load) The goal of the balancing process is then to select optimal increments (incr) and decrements (decr) around these day-ahead positions to meet deviations in the real-time load (which may be higher or lower than the load that defined the day-ahead positions) 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Day Ahead & Real Time modelling (cont) The input pairs Generator Offer Quantity, and Offer Price are used to define the incr and decr offers. Positive quantities are interpreted as offers to increment the generator's dispatch above the base quantity, while negative quantities are interpreted as offers to decrement the generator's dispatch. Generally the first offer band equals the day-ahead generation (Offer Base) with a price equal to the negative of the incremental cost of generation to that point. Subsequent bands are priced at the incremental cost of moving the generation above the Offer Base. Note that Offer Price is usually positive for both incr and decr parts of the offer, except in cases where negative offer prices are being used to keep generation above Min Stable Level. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Can model 1-minute or greater time step Real-time markets Sequential Day-ahead and Real-Time markets simulation to capture the Renewables / load variability and uncertainty DA simulation produces unit commitment schedules using forecasted Renewable generations and loads RT simulation reveals the ramp capacity adequacy using “actual” Renewable generations and loads 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS Program Scope 1 min 30 years+ 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

The EU Electricity market Target Model
DRAFT CACM NETWORK CODE – SUPPORTING DOCUMENT (Source: ENTSO-e) 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Linking Day ahead and Balancing Market in PLEXOS 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Linking Day ahead and Balancing Market Solutions
5/11/2011 Linking Day ahead and Balancing Market Solutions The simulator provides a means for the solution of one Model to act as input for subsequent Models via the Data File class. This is particularly useful for modelling balancing (or real-time) markets because it allows the Generation and other solution values to be passed from the day-ahead simulation to the real-time simulation without any manual intervention or data processing. New Feature in PLEXOS: ‘Interleaved Mode’ What is unique about this run mode is that the simulation steps are interleaved, and initial conditions are passed back from the second simulation (e.g. real-time) to the first (e.g. day-ahead) at each stage. The data are passed between simulations using text files written when the Report Write Flat Files option is set. For the day-ahead Model select all the outputs that will act as inputs to the real-time simulation. These might include: Generator Generation Generator Undispatched Capacity Generator SRMC The Model will then write out these files into a folder structure. The interval solution values e.g. period-by-period Generation appear in the files: Model DA Solution\interval\ST Generator(*).Generation.csv where "DA" is an example Model name, and the * represents the numeric index of each Generator in the system. Generator names are not used to name these files and instead the index must be looked-up in the reference file "id2name.csv" which is created in the root of the text solution folder. It would be time consuming to do this look-up every time you wanted to pass solution data to another model, so instead the Data File class supports the wildcard syntax in the Filename field directly, allowing you to 'point' to the day-ahead solution files with a single line of input. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Flow Diagram of Interleaved Run Mode

DA/RT Balancing Model Description Generator Units Max Capacity MSL VO&M Start Cost G1 1 400 75 27 7500 G2 300 50 36 1000 G3 700 150 18 10000 Datafile definitions link to DA Model Datafile Filename Available Capacity Model DA Solution\interval\ST Generator(*).Available Capacity.csv Generation Model DA Solution\interval\ST Generator(*).Generation.csv Price Received Model DA Solution\interval\ST Generator(*).Price Received.csv SRMC Model DA Solution\interval\ST Generator(*).SRMC.csv Undispatched Capacity Model DA Solution\interval\ST Generator(*).Undispatched Capacity.csv Units Generating Model DA Solution\interval\ST Generator(*).Units Generating.csv 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

DA/RT Balancing Input to Real time model Property Value Uniits Band Escalator Datafile Scenario Commit -1 - 1 Units Generating RT offers and bids Offer Base MW Generation Offer Quantity x -1 2 Undispatched Capacity Offer Price €/MWh SRMC x 1.01 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

PLEXOS® Modelling Tour Handling Different Horizon Resolutions between Day-Ahead & Real Time Markets. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Where the day-ahead market is simulated at a different resolution to the real-time market, the text data passed between the models are automatically up scaled or downscaled. The method used for up/downscaling is controlled by the Data File Upscaling Method, when increasing the resolution in real-time compared to day-ahead, vice versa for Downscaling Method. It is important to choose the correct method depending on the property that is the target of the data. For example, Generator Offer Quantity suits up scaling using "Interpolation", whereas Units Generating suits up scaling using method "Step". 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Wind and Dynamic Constraints
Wind resources require significant ramping capability in the system PLEXOS allows for the specification of ramp rates which must be obeyed (or can be violated at a penalty) In the long run ramp rates ensure that enough flexible capacity is built to meet the ramping requirement imposed by wind energy In the short run of DA & RT Markets this ensures PLEXOS commits enough fast ramping plant to meet requirements 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

EXAMPLE - Modelling Details
Unit Commitment In hourly modelling it is convenient to assume that generators ‘jump’ from zero generation to Min Stable Level in the one hour and back again when shutting down. This simplifies the unit commitment problem because only operation inside the normal operating range (Min Stable Level to Max Capacity) needs to be modelled. In 5-minute modelling the time taken for a unit to run up is important both because of simulation accuracy and also because units cannot provide regulation or other ancillary services while running up or down. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

EXAMPLE (based on case study) - Modelling Details Property Value Units Max Capacity 100 MW Min Stable Level 40 Max Ramp Up 1 MW/min. Run Up Rate 0.5 A more detailed alternative to constant Run Up Rate is a Start Profile. In the following definition “P” indicates the interval number after the unit is commenced start up: Property Value Units Timeslice Start Profile 5 MW P01 10 P02 15 P03 20 P04-13 25 P14 30 P15 35 P16 40 P17 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

A ‘basic’ model of spinning reserve response assumes that response is defined only by the constraint: Response <= Spare Capacity In reality (and in 5-minute modelling) response is limited by the rate of response in the timeframe the reserve is required in: Response <= Timeframe × Energy Ramp Rate And further, energy ramping subtracts from available reserve response, but not necessarily at a 1:1. The reserve response can be faster than the energy ramping rate: Ramp + [Response Ratio] × Response <= Timeframe × Energy Ramp Rate In addition, no response is possible during the run up or down period. In the following example the [Response Ratio] parameter is set to 0.5. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

When considering multiple ancillary services such as spinning reserve and regulation reserve, these services are “mutually exclusive” in that spare capacity set aside for regulation cannot be used to provide spinning reserve. This constraint must also be reflected in the mathematical formulation. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Some interesting findings of the study: Hourly modelling does not provide sufficient resolution to accurately estimate reserve shortages when significant amounts of wind generation are on the system. 5-minute modelling over very large-scale system is a practical reality with modern simulation codes. Modelling that ignores the combined energy ramping and reserve response interaction and contingency-constrained unit commitment underestimates the magnitude and frequency of reserve shortages (periods of time of inadequate reserve response): Whereas hourly modelling indicated no reserve shortages; 5-minute showed some shortage 5-minute considering energy-ramp constraints and contingencies showed shortages doubled 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Additional Features - System Reliability
Using the Projected Assessment of System Adequacy (PASA) Phase PLEXOS can be used to examine the reliability of given build plans and renewable uptake including: Loss of Load Probability, Loss of load Expectation, Expected Demand not served, Expected Energy not served This output can be used to analyse minimum capacity reserve margins and capacity requirements to meet system stability requirements. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

Conclusion - System Reserves Policy
5/11/2011 Conclusion - System Reserves Policy Uptake of renewable energy such as wind can result in larger requirements for system reserves. Using the ancillary services features of PLEXOS the policy maker can: Optimise the uptake of renewables given this additional burden Ensure provision of reserves in dispatch and expansion planning Calculate the cost to the system and effect on energy prices of the additional reserve requirements Calculate expected ancillary service prices. This analysis takes advantage of PLEXOS’s ability to set dynamic reserve requirements based on generator, load or line contingencies. 18/09/2018 3rd European Electricity Ancillary Services & Balancing Forum Workshop

European Electricity Ancillary service & Balancing Forum- Berlin 2012
Thank you for your time, attention and the opportunity. 18/09/2018 European Electricity Ancillary service & Balancing Forum- Berlin 2012

Use of PLEXOS to Assess the Operational And Costs Effects of Grid Integration, Ancillary Services and Balancing Dr Christos Papadopoulos Regional Manager.

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Use of PLEXOS to Assess the Operational And Costs Effects of Grid Integration, Ancillary Services and Balancing Dr Christos Papadopoulos Regional Manager.

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Presentation on theme: "Use of PLEXOS to Assess the Operational And Costs Effects of Grid Integration, Ancillary Services and Balancing Dr Christos Papadopoulos Regional Manager."— Presentation transcript:

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