A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid Prepared By: Sakeena Javaid (PhD Scholar) Supervised.

A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid Prepared By: Sakeena Javaid (PhD Scholar) Supervised By: Dr. Nadeem Javaid Comsats Institute of Information Technology, 44000, Islamabad, Pakistan.

Overview Introduction Literature Review Objectives Problem Formulation
Proposed Solution Results and Discussions Conclusion

Introduction (1/2) Robust and more reliable power grid [1]
Researchers are focused on peak demand DSM programs facilitate grid’s functionalities [2] Electricity market control Infrastructure maintenance Management of decentralized energy resources Load shapes depict electricity demands (either daily or seasonal) [3], [4] Industrial or residential consumers use electricity in PHs and OPHs Load shapes can be modified DSM: Demand Side Management PHs: Peak Hours OPHs: Off Peak Hours PAR: Peak to Average Ratio UC: User Comfort [1] Logenthiran, T., Srinivasan, D., and Shun, T. Z. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid 2012, 3(3), pp [2] Peter, Palensky and Dietmar, Dietrich, D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE transactions on industrial informatics, 2011, 7(3), pp [3] Maharjan, I. K. Demand side management: load management, load profiling, load shifting, residential and industrial consumer, energy audit, reliability, urban, semi-urban and rural setting. LAP Lambert Academic Publication, 2010. [4] Gellings, C. W. and Chamberlin, J. H. Demand side management: Concepts and Methods. Liburn, GA: Fairmont, (1988).

Table1: Benefits of Demand Side Management
Introduction (2/2) Peak clipping Valley filling Load shifting Strategic conservation Strategic load growth Flexible load shape Working of a generic DSM controller Electricity cost minimization Energy consumption PAR minimization UC maximization Customer Benefits Utility Benefits Societal Benefits Satisfy electricity demands Lower cost of service Reduce environmental degradation Reduce/stabilize costs or electricity bill Improve operating efficiency, Flexibility Conserve resources Maintain/improve lifestyle and productivity Improve customer service Protect global environment Table1: Benefits of Demand Side Management

Literature Review (1/4) Technique for controlling the residential loads [5] Maximizing UC and Minimizing electricity bill A fully automated EMS [6] Residential and commercial buildings Used Q-learning algorithm Optimal DR mechanisms Cristopher et al. [7] design a new framework For home energy management Concept of load clustering First cluster is from 1am to 7am Second is from 8 am to 3 pm Third is from 3pm to midnight EMS : Energy Management System GA: Genetic Algorithm RSM: Realistic Scheduling Mechanisms BPSO: Binary Particle Swarm Optimization DR: Demand Response RTP: Real Time Price SG: Smart Grid EMS: Energy Management System [5] Rasheed, M. B., Javaid, N., Ahmad, A., Khan, Z. A., Qasim, U., and Alrajeh, N. An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes. Applied Sciences, 2015, 5(4), pp [6] Wen, Z., O’Neill, D. and Maei, H. "Optimal demand response using device-based reinforcement learning". IEEE Transactions on Smart Grid, 2015, 6(5), pp [7] Adika, C. O. and Wang, L. Smart charging and appliance scheduling approaches to demand side management. International Journal of Electrical Power and Energy Systems, 2014, 57, pp

Literature Review (2/4) Optimal appliance scheduling [8]
For minimizing the energy price Under dynamic pricing scheme to avoid PHs GA is used to solve the scheduling problem [9] Use RTP pricing schemes In residential, commercial, and industrial areas RSM [10] deals with Improving UC Scheduling of the appliances using BPSO [8] Shirazi, E. and Jadid, S. Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy and Buildings, 2015, 93, pp [9] Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad, K. and Ahmad, I. An Efficient Genetic Algorithm Based Demand Side Management Scheme for Smart Grid. In Network-Based Information Systems (NBiS), th International Conference on, 2015, pp [10] Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z. A., Qasim, U., Ahmed, I. and Ilahi, M. Realistic Scheduling Mechanism for Smart Homes. Energies, 2016, 9(3), pp. 202.

Literature Review (3/4) DR mechanisms [11] For residential users
To minimize electricity bills Maximize the UC and privacy An energy management approach considers household users [12] Each house using two types of requests Essential and Flexible (delay sensitive, delay tolerant) The dynamic load priority method presents priorities in [13] Modify load priorities Demand response events (i.e., occurrence of events) [11] Safdarian, A., Fotuhi-Firuzabad, M. and Lehtonen, M. Optimal Residential Load Management in Smart Grids: A Decentralized Framework. IEEE Transactions on Smart Grid, 2016, 7(4), pp [12] Liu, Y., Yuen, C., Yu, R., Zhang, Y. and Xie, S. Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Transactions on Smart Grid, 2016, 7(3), pp [13] Fernandes, F.,Morais, H., Vale, Z. and Ramos, C. Dynamic loadmanagement in a smart home to participate in demand response events. Energy and Buildings, 2014, 82, pp

Table 2: Achievements and drawbacks in existing techniques
Literature Review (4/4) Table 2: Achievements and drawbacks in existing techniques Techniques Targeted Area Achievements Drawbacks Efficient GA Based DSM Scheme for SG [9] Residential, Commercial and Industrial Areas Cost Minimization Inconsideration of UC Efficient Power Scheduling Scheme for Residential Load Management [5] Residential Energy Management Cost Minimization, PAR reduction and UC Maximization Explicit use of Pressure Values Causes System’s Low Performance Optimal Residential Appliance Scheduling under Dynamic Pricing Scheme via HEMDAS [8] Home Energy Management Cost Minimization and UC Maximization Inconsideration of the Initial Installation cost Queuing-based Energy Consumption Management for Heterogeneous Residential Demands in SG [12] Residential SG Networks Cost Minimization and Delay Reduction Inconsideration of Parameters Tuning Optimal Residential Load Management in SGs: A Decentralized Framework [11] Residential Customers Energy Efficiency Inconsideration of Cost

Problem Formulation (1/3)
Xi,t: Status of the appliance (ON, OFF) PRi,t: Price of the energy consumed at time interval ‘t’ H1: PHs {7,8,9,10} H2: OPHs (all others except H1) Problem is formulated as multi-objective Cost Minimization

EappUtil: Minimum appliance delay EcostSavings: Total cost savings Sch_cost: Scheduled cost Max. cost: Maximum cost UC Maximization

Multi-objective optimization problem is evaluated by Using the cost optimization (eq. 1) UC optimization (eq. 2) Here, c1=c2=0.5

Proposed Solution(1/10) Proposed DSM techniques deal with the load management In residential area For single home Multiple homes Architecture consists of Number of homes SMs AMI HG Utility

Proposed Solution(2/10) Three types of appliances are under consideration Fixed or non-shiftable Elastic (shiftable, however, without altering their LOT) Shiftable (shiftable) Appliances are also categorized in classes Class A (non-shiftable) Class B (shiftable)

Proposed Solution(3/10) RTP tariff model is used Fig. 3 shows
Appliances’ schedule is generated by EMC Using the given frame format Frame format consists of an eight bit pattern Class ID, appliance ID, scheduling bit, interruptible or non interruptible bit and priority bit (PB) EMC schedules and checks appliances’ PB

Proposed Solution(4/10) Five algorithms are implemented
GA, BPSO, WDO, BFOA and hybrid GWD Algorithms Scheduling Procedure The load is shifted to the off peak hours PBs are used to avoid peaks during the off peak hours Each appliance is using a PB PB indicates status (either ON or OFF) PB can be specified as; (0,1) PB is communicated via an RTP frame format BFOA: Bacteria Foraging Optimization Algorithm WDO: Wind Driven Optimization GWD: Genetic-Wind Driven LOT: Length of Operation Time li: Counter for LOT t: Time slots P: Population Size H: Total Number of Homes SM: Smart Meters AMI: Advance Metering Infrastructure HG: Home Gateway UC: Utility Companies

Table 4: Modifications and Expected Outcomes
Proposed Solution (5/10) Algorithm 1: GA Algorithm Initialize Parameters Generate feasible population randomly Calculate fitness function using Eqn. (3) Select the best solutions in P Check PHs and swap(OPH, PH) if energy consumption is high check appliance PB Perform crossover operation Perform mutation operation Generate new population Table 4: Modifications and Expected Outcomes

Table 5: Refinements and Expected Outcomes
Proposed Solution (6/10) Algorithm 2: BPSO Algorithm Initialize parameters Randomly generate population of particles Set p_best and g_best values of the particles Evaluate fitness function using Eqn. (3) Check PHs and swap(OPH, PH) if energy consumption is high check appliance PB Update p_best and g_best values Table 5: Refinements and Expected Outcomes

Table 6: Adaptations and Expected Outcomes
Proposed Solution (7/10) Algorithm 3: WDO Algorithm Initialize Parameters Generate initial random population Assign random positions and velocities to air particles Evaluate fitness of each air parcel Eqn. (3) Check PHs and swap(OPH, PH) if energy consumption is high check appliance PB Update velocities and positions Table 6: Adaptations and Expected Outcomes

Table 7: Refinements and Expected Outcomes
Proposed Solution (8/10) Algorithm 4: BFOA Initialize parameters Randomly initialize the swarm of bacteria Perform chemotactic procedure Check reproduction process by swapping Perform the elimination-dispersal by elimination Evaluate objective functions using Eqn. (3) Check PHs and swap(OPH, PH) if energy consumption is high check appliance PB Table 7: Refinements and Expected Outcomes

Proposed Solution (9/10) Genetic-Wind Driven Algorithm
WDO’s steps are performed in a similar way Explained in original algorithm Pressure values become very large The updating velocities exceed Diminishing WDO’s performance Velocity updating steps for the global air pressure are replaced With GA’s crossover and mutation operations Scheduling procedure is followed as the same In GA, BPSO, BFOA and WDO Evaluated with the same objective function

Table 8: Modifications and Anticipated Outcomes
Proposed Solution (10/10) Algorithm 5: GWD Algorithm Initialize parameters Randomly generate initial population Assign random positions and velocities to air particles Evaluate fitness of each air parcel using Eq. 3 Check PHs and swap(OPH, PH) if energy consumption is high check appliance PB Apply crossover and mutation operation Update velocities and positions Table 8: Modifications and Anticipated Outcomes

Results and Discussions (1/9)
Conducted in MATLAB Using RTP pricing scheme 24 hour time period is divided Into PHs and OPHs Four hours are taken PHs (from 7 PM to 10 PM) [40] Considered two simulation scenarios Single home Fifty homes To evaluate the performance Performance metrics Cost Energy Consumption PAR UC [40] Electricity Tariff. Available online: (accessed on 2 April 2016).

Table 9: Appliances and Classes

For unscheduled case, scheduled GA, WDO and hybrid GWD Maximum energy consumption values 16.2 kWh, 11.8 kWh, 8.2 kWh and 4.1 kWh Energy consumption in GA, WDO and GWD 56.89%, 67.18% and 65.87% Obtained by dividing the scheduled cost and unscheduled cost with percentage

Maximum amount of electricity bill In unscheduled case is cents as shown in Fig. 6 Reduced to 78 cents in the case of GA Reduced to 245 cents in WDO Reduced from 318 to 75 cents in GWD Electricity cost in GA, WDO, and GWD 60%, 30% and 62% During PHs sufficient electricity cost reduction is achieved GWD performs better than the other algorithms in cost reduction

PAR performance of all algorithms (GA, WDO and GWD) is shown in Fig. 7 PAR is significantly reduced In hybrid GWD as compared to the GA, WDO and unscheduled case PAR graph for GA, WDO and hybrid GWD displays Power consumption of appliances Load is optimally distributed without creating peaks during the OPHs The PAR in GA, WDO, GWD is 60%, 75% and 40%.

Desired UC is achieved (Fig. 8) Significantly reduced for GWD, GA and WDO Scheduled WDO, GA and GWD is 60% Maximum delay considered here is 4 hours Trade-off exists in UC of all scheduled algorithms Performance of this work is much better By considering the priority bits Minimum delay Figure 8: User Comfort

Energy Consumption (kWh)
Results and Discussions (7/9) For Fifty Homes Results of energy consumption, cost, PAR and UC Techniques Energy Consumption (kWh) Cost (cents) PAR UC Scheduled Unscheduled GA 15.00 16.5 125.20 350 26% 100% 0.9 BPSO 7.90 175 25% 0.5 WDO 11 215 12% 0.55 BFOA 14.5 160 2% 0.85 Table 10: Results of energy consumption, cost, PAR and UC

Figure shows that BPSO has maximum computational burden (execution time=88 sec) BFOA has the minimum computational burden (execution time=8 sec) a difference of 80 sec GA, WDO and GWD take 13 sec 43 sec and 32 sec (to execute) GWD pays the cost of moderate execution time

Table 11: Trade-offs in Proposed Algorithms

Conclusions DSM controller designed in which five heuristic algorithms
(GA, BPSO, WDO, BFOA, and our proposed hybrid GWD) are implemented Hybrid GWD scheme reduced electricity cost By approximately 10% in comparison to GA and 33% to WDO GA provided the global optimal (in scheduling even when population size is large) GA outperformed BPSO, WDO and BFOA in terms of Electricity cost Energy consumption Explicit pressure values make WDO the slowest To converge among all algorithms Trade-off in user comfort exists in hybrid GWD case

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A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid Prepared By: Sakeena Javaid (PhD Scholar) Supervised.

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A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid Prepared By: Sakeena Javaid (PhD Scholar) Supervised.

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Presentation on theme: "A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid Prepared By: Sakeena Javaid (PhD Scholar) Supervised."— Presentation transcript:

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