Optimizing Operations Holistically for Maximum Savings Simon Bunn CTO - Derceto

Water, Energy and Sustainability “The more than 60,000 water systems and 15,000 wastewater systems in the United States are among the country’s largest energy consumers, using about 75 billion kWh/yr nationally —3 percent of annual U.S. electricity consumption.“ Electric Power Research Institute, Energy Audit Manual for Water/Wastewater Facilities, (Palo Alto: 1999), Executive Summary That’s $10 billion in energy costs per year! “Water services are monopolistic by nature (on a local level) and are not naturally driven to increase efficiency and achieve best practices” International Water Association (IWA) 2008

Sustainability for Water Utilities Water Systems use large amounts of energy. According to some US based studies*1, on average 80% of the marginal costs is for electricity. New treatment process demand higher energy levels, ozonation, UV disinfection, desalination Climate change introduces new issues such as droughts, reducing inflows, and flooding disrupting delivery. While wastewater utilities have tackled energy management via bio-gas fed generators the water utilities have made more limited steps 1. Ghimire, S. and Barkdoll,B. (2007) “Issues in energy consumption by municipal drinking water distribution systems” World environmental and water resources congress 2007 ASCE

Operations Optimization Goals AWWA RF Energy and Water Quality Management System project (EWQMS) starting in 1993 with a functional specification released in 1997 defining: Interface with SCADA System State Estimator ‘Data Scrubber’ Water Demand Forecaster Water Quality Module Energy Cost Calculator Pump Schedule Optimization System Monitoring & Alarms No complete systems delivered as of today

Why Not? The Optimization Problem Self modifying behaviour, starting an additional pump at a treatment plant can change pressure everywhere Water quality requirements, flow smoothing Leakage, pressure control Poor quality of input data (SCADA and demand) Reliability of outputs, pump failures, telemetry issues Flexibility to deal with planned and unplanned outages Non-linear pump curves, 2 pumps don’t give twice the flow of one pump With 11 pumps at ½ hour schedules there are 2^69 possible combinations, more than all the atoms in the entire universe

Need for a holistic optimizer Able to handle Water quality issues Tariffs from Flat Time Of Use spot market Electricity Demand charges (peak kW or kVa) with ratchet clauses and real-time market requirements Treatment plant marginal cost for production, which can vary due to raw water quality, time of day etc Alternative pumping path options Non-linear pump efficiency, plus parallel pump efficiency at each pump station and globally Solve all pump stations together, willing to sacrifice cost at one site if overall benefit is greater system-wide

Aquadapt software Operator Panel 201 Operations Simulator 209 PC on LAN Operator Panel 201 Operations Simulator 209 Hydraulic Model 208 Water Utility SCADA System Current day / real-time Data Cleaner 206 SCADA Interface 203 (Live Server) Primary Database (B/U Server) Historian PC on LAN Application Manager 218 PC on LAN Dashboard 210

What does Aquadapt do? Interfaces directly to existing SCADA to both read input values and write pump schedules – fully automatic Targets five areas simultaneously: Electrical load movement in time, to maximise utilisation of low cost tariff blocks Electricity peak demand reduction. Utilisation of lowest production and chemical cost sources of water. Utilisation of shortest path between source and destination Energy efficiency improvements from pumps and pumping plants. Solves holistically for all costs simultaneously This slide is probably the most important. What does Derceto do? Derceto is a scheduling system. It is designed to schedule pumps in half hour blocks of time for the next 24-48hrs of time to achieve lowest cost to distribute water. Derceto has to do this within all your operational constraints. For example: min/max storage tank levels that you are willing to exercise, the allowable operational range of the tank The rates of change that is allowed at WTP Water quality requirements Even though we work within these operational requirements, there is always money on the table. This is because most systems operate very conservatively by operators. Operators like to: Keep storage tanks full WTP flow rates constant With tariff structures, it is more economical to fill tanks when tariffs are low and use gravity to drain tanks when it is expensive. This is why there is usually a significant amount of savings for most systems. The interesting thing about Derceto is that a lot of people, a lot of academics have had a look at this problem and most have found that it is insolvable. Some people have tried Genetic Algorithms which is basically throwing hundreds and hundreds of darts at a dart board and hope that one finds the right solution. We have found a completely different way to solve it. This means that Derceto: Is extremely fast, some systems takes days or if really good hours to find a solution. Derceto normally takes less than 2mins to find a solution. This means that it can runs in real time Can run interactively with your operators Can run in a real-time energy market which is the way the world is going Derceto solves the mass balance problem first. That is to say that you are in the business of delivering water, not saving energy. Therefore, Derceto first constraint is that it must find a solution that delivers water and also replenishes the storage tanks because the lowest cost solution is not to pump at all. It is really good for day1 but not so good for day 2. Derceto obeys the mass balance rule but looks at the lowest cost energy tariff period to minimize energy costs. One interesting thing about Derceto is that the problem of distributing water is not linear. Derceto looks at maximizing the energy efficiency of pumps. Just because you buy a pump and it is rate at 1MGD at 400kW, it does not mean that is what it will use. A pump has a system curve and a pump curve. Where those curves intersect is what you will get from that pump. So at some times of the day you might only get 0.8MGD and you might be using more kW. Derceto knows this and Derceto will choose the most optimum way to run that pump to increase its efficiency. Water Quality. One of the outputs of Derceto is to turn tanks over, it tends to be the lowest cost solution. This is because if you use the full operational range of the tank you are likely to reduce or not pump during the expensive tariff periods. Cycling tanks usually provides lower water age which is normally consistent with better water quality. We do not guarantee water quality but if you have certain rules for maintaining water quality we can embed those rules inside Derceto. This will mean that Derceto will consistently meet those rules. One of the problems with turning tanks over is that it is usually a multiday strategy which means it covers multi-shifts. Sometimes the next shift is unaware what part of the cycle strategy you are in and sometimes will fill a tank when the previous shift was trying to drain the tank. Loss of consistency. Though Derceto predicts a 24-48hrs schedule, every half hour Derceto will find a new solution. The reason is that Derceto is adapting to: Changing weather patterns = changing water demand Equipment availability Availability of WTP and production flows. If WTP operator rings up and says I have lost a clarifier and need to drop by 60MGD, Derceto will take that into account and find a new solution 2mins later.

Case Study 1: East Bay MUD System installed 2003 Achieved 13% energy cost savings 180 Distribution Reservoirs 3 Aqueducts ( 90 mi/147 km ) 7 Water Supply Reservoirs 6 Water Treatment Plants 25 Rate Control Valves 135 Pumping Plants 3900 Miles of Distribution Pipeline 2 Hydroelectric Plants 220 MGD Average 1.3 million Customers

Case Study 1: East Bay MUD EBMUD were already avoiding TOU tariffs

Case Study 1: East Bay MUD So the holistic optimizer targeted efficiency

Pump station efficiency improved universally

Pumps operate more efficiently

Case Study 2: Wellington NZ In late 2001 New Zealand was at the end of a major drought that reduced hydro storage levels to critical low points 70% of New Zealand’s power generation was from hydro

Case Study 2: Wellington NZ Greater Wellington Water contact Derceto and asked if Aquadapt could be changed to target efficiency instead of lowest energy cost. This merely involved changing all tariffs to a single flat tariff and removing demand charges Result was an immediate additional 6% reduction in kWh consumption to deliver the same water volume Increased energy cost overall by about 2% The tariff change was reversed after the crisis ended The public energy company thanked Greater Wellington Water …but still charged the extra cost

Case Study 3 : Northumbrian (UK) Northumbrian Water provide water and sewage services to 4.4 million people in the UK 64 water Treatment Works 362 Pump Stations 356 Reservoirs Analyzed savings for this major utility using a closed loop optimization approach Analysis showed significant savings but UK energy rates increasing at 17% per year New energy provider with higher rates proposed What would the impact on savings be?

Case Study 3 : Northumbrian (UK)

Case Study 3 : Northumbrian (UK)

Case Study 3 : Northumbrian (UK) Savings actually increased (but so does energy cost) Source shift = £167,500 or 70.2% Load shift = £71,100 or 29.8% What if a flat tariff was introduced? But what if source shifting (using different total production volumes at each treatment plant) was not possible? Almost no drop in savings £391,700 or 84.5% as electrical load shifting increased 270% to fill the gap

Derceto AQUADAPT Utility Case Studies – USA * Factory Tests Complete – Projects being installed now

Conclusions Operations optimization and especially energy management can lead to substantial savings Focusing on just tariff or single pump stations can be counter productive A global holistic optimizer takes into account; Water quality Tariff Production costs (chemical and electrical) Constraints Savings are not independent, removing one area of potential savings can lead to improvements in other areas with little impact on overall savings

Simon Bunn sbunn@derceto.com Wes Wood wwood@derceto.com Thank You Simon Bunn sbunn@derceto.com Wes Wood wwood@derceto.com