Static and dynamic Economic load dispatch
- 1. Introduction • Renewable energy sources found in nature that are self regenerating: • These sources are normally used to produce clean (or green) energy. This production does not lead to climate change and does not involve emission of pollutants. • A related term is sustainable energy: this concept refers to generating energy with an awareness of the future, i.e. in a way that would enable future generations to meet their energy needs too.
- 2. • Micro grids: Micro grids are small scale low voltage electrical distribution networks composed of distributed energy resources (DERs), such as distributed generation systems (DGs), energy storage systems (ESS), controllable and non- controllable loads.
- 3. • Components of Microgrid – Distributed Generation – Loads – Energy storage systems – Static disconnect switch – Controller – Mode switching device – Point of Common Coupling
- 4. • Need of Microgrid – Microgrid could be the answer to our energy crisis. – Transmission losses gets highly reduced. – Microgrid results in substantial savings and cuts emissions without major changes to lifestyles. – Provide high quality and reliable energy supply to critical loads.
- 5. Economic Load Dispatch ELD: To optimally distribute the total power demand among the generating units Minimizing the selected cost criteria Subject to load and operation constraints. Conventional methods for solving ELD problem Lambda iteration method Gradient based method Dynamic programming method Linear and non linear programming
- 6. Power balance constraints: considering power output of the non-dispatchable sources and power injected by the main grid: Modification of the Constraint ∑ 𝒊=𝟏 𝑵 𝑷𝑮𝒊+𝑷𝑴𝒂𝒊𝒏=∑ 𝒅=𝟏 𝑳 𝑷𝑳𝒅− ∑ 𝒌=𝟏 𝑵𝑵𝑫 𝑷𝑵𝑫𝒌 …..(5)
- 7. Spinning reserve requirement constraints: The spinning reserve requirement is reflected as a decrease in the maximum limit and an increase in the minimum limit of the first DG within each area: Modification of the Constraint 𝑷𝑮𝟏 ≤ 𝑷𝑮𝟏 𝒎𝒂𝒙 −( 𝒓 𝟏𝟎𝟎 ∑ 𝒅=𝟏 𝑳 𝑷𝑳𝒅+ 𝒖 𝟏𝟎𝟎 ∑ 𝒌=𝟏 𝑵𝑵𝑫 𝑷 𝑵𝑫𝒌) 𝑷𝑮𝟏 ≥ 𝑷𝑮𝟏 𝒎𝒊𝒏 −( 𝒓 𝟏𝟎𝟎 ∑ 𝒅=𝟏 𝑳 𝑷𝑳𝒅+ 𝒖 𝟏𝟎𝟎 ∑ 𝒌=𝟏 𝑵𝑵𝑫 𝑷𝑵𝑫𝒌) …..(7) …..(6)
- 8. • ELD problem in microgrid addresses various issues like single and multi objective optimization problems, various system constraints, highly constrained and non-linear problem, fluctuating and variable nature of RES like PV and wind, demand response of consumers, combined heat and power dispatch (CHP) with losses and economic emission dispatch (EED).
- 9. • Comparative and critical analysis for economic dispatch research is concluded. • The various Economic Load Dispatch problems are 1. Economic load dispatch with valve point loading (EDVP) effects 2. Multi-area economic load dispatch (MAED) 3. Combined economic-emission dispatch (CEED) 4. Security constrained economic load dispatch (SCELD)
- 10. Static and Dynamic ELD • Static economic dispatch gives the economic dispatch solutions for a static or fixed load conditions. • For Economic dispatch problem it is assumed that the load is fixed for 24 hrs. • In dynamic economic load dispatch problem a varying load for 24 hrs of the day is considered.
- 11. • The dynamic economic dispatch problems differ from the static economic dispatch problem by incorporating ramp rate constraints of generators. • The static scheduling model is only suitable for some simple structured MGS. • With the increase in the number of microsources and diverse types of load within the MG, the static economic dispatch model is no longer applicable to the rapid developed MG system Static and Dynamic ELD
- 12. • Researchers have made improvements and innovations on the basis of static economic scheduling. • As a special load, the electric vehicle (EV) has both the characteristics of the load and the power source, EV is used as a simple energy storage unit to participate in the coordinated control between micro sources in the MG. Static and Dynamic ELD
- 13. Static and Dynamic ELD Ref. No. Optimization technique Objective function Case study/Test system Dynamic ELD : MG dynamic economic dispatch considering uncertainty 22 Improved PSO algorithm combined with Monte Carlo simulation operation cost and the pollutant treatment cost of the microgrid system CHP microgrid system that includes wind turbines, photovoltaic arrays, diesel engines, a micro-turbine, a fuel cell and a battery source. 23 Grey Wolf Optimizer (GWO), modified version (MGWO), MGWO amalgamated with particle swarm optimization (PSO), sine cosine algorithm (SCA) and crow search algorithm (CSA) Minimize the generation cost. Two fossil fueled generators, two wind turbines and three fuel cells are the DERs. 24 Harmony Search Algorithm Minimize the power generation cost, to establish coordination between different DGs over many periods considering dynamic grid cost. Micro-grid system comprising various DG technologies such as wind turbines, photovoltaic, diesel engine and a fuel cell. 25 Monte Carlo simulation combined with particle swarm optimization Minimizing the generation cost The controlled microsources, including DG, MT, and FC, are assumed to track rapidly the variations of LD, WT, and PV.
- 14. Optimization based ELD • The cost benefit of any MG may not be completely justified, without optimization techniques. • Optimization aims at identifying the best alternative from a set of specified solutions that are the most cost-effective. • Many approaches are available for addressing optimization problems like classical optimization techniques, evolutionary or bio inspired algorithms and ANN etc. • The various class of optimization techniques used for ELD are given as below in tabular form.
- 15. Based on Metaheuristic Method Ref. No. Optimization technique Objective function Case study/Test system ELD based on bio inspired / meta heuristic Algorithms 13 Genetic Algorithm Minimize operation cost, emission cost and maximize energy trade profit Grid connected Microgridcomprises of Micro-turbine, wind, Fuel cell and Battery storage 14 Particle swarm optimization Minimize operation cost. GridconnectedMicrogrid including fuel cell, gas-fired microturbine, windturbine, photovoltaic and energystorage devices. 15 Particle swarm optimization Operation, emission and reliability costs of MG are Minimized MG including 3 feeders, photovoltaic, wind turbine, fuel cell, micro turbine, diesel engine and battery storage. 16 Ant colony optimization Operational cost of MG, which includes bidding cost of DERs, penalty cost on load shedding and DR incentives, is minimized. Stand-alone wind turbine, photovoltaic, microturbine, and energy storage system. 17 Gravitational search algorithm Operating cost of an isolated MG is minimized Stand-alone wind turbine, photovoltaic, microturbine , and energy storage system.
- 16. Security Constrained ELD • The increasing penetration level of renewable energy sources (RESs) such as wind and solar energy, have enhanced uncertainties so the traditional deterministic decision making in the electric power industry is gradually shifting towards stochastic decision. • The intermittent and non-dispatchable renewable like wind and solar exhibits sub- hourly fluctuations.
- 17. • This motivates the need for optimization at multiple timescales. RESs are highly site- specific, stochastic in nature and are fairly evenly distributed around the world with little or no costs. • They are greatly dependent on the climatic conditions, geographical factors and seasons of the site under consideration.
- 18. Ref. No. Optimization Type/ tools Objective function Case study/Test system 1 MO-SCOPF, HPSO-APO Minimize total production cost, Minimize active power loss and Maximize security level IEEE 30 bus system an Practical Indian 75 Bus system 4 MO, SCED Minimize deviation of transactions and Minimize operating cost of generation IEEE 24 Bus system 6 GAMS, SNOPT Minimize production cost and Maximize security level IEEE 30 Bus system 2 MO SCED, HOMER, MATLAB Minimize cost of electricity Maximize utilization of resources IEEE test systems 7 MO RELD- (NSGA-II) Optimize annualized cost and Optimize RELD Belgium’s electricity transmission system 3 MOSMPC SCED-OCD Optimize operating cost and Maximize security level Modified WECC 9-bus test system 5 CED-IRES Optimize operating cost and Maximize security level IEEE 39 Bus system Security Constrained ELD
- 19. References 5. H. Li, H. Cui, Z. Ma and Y. Chai, "Security-constrained economic dispatch of wind power integrated power system based on interval optimization," Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015, 2015, doi: 15.2015.248 6. G. Li, R. Zhang, H. Chen, T. Jiang, H. Jia, Y. Mu, et al., “Security- Constrained Economic Dispatch for Integrated Natural Gas and Electricity Systems,” Energy Procedia, Volume 88, 2016, Pages 330-335, ISSN 1876-6102 7. Hasnae Bilil, Ghassane Aniba, Mohamed Maaroufi, “Multiobjective optimization of renewable energy penetration rate in power systems,” Energy Procedia, Volume 50, 2014, Pages 368-375, ISSN 1876-6102, 8. M. H. Amrollahi, S. M. T. Bathaee, “Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response, Applied Energy, Volume 202, 2017, Pages 66-77, ISSN 0306-2619,
- 20. References 9. N. Anglani, G. Oriti and M. Colombini, "Optimized energy management system to reduce fuel consumption in remote military microgrids," 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, 2016, pp. 1-8, doi: 10.1109/ECCE.2016.7855323. 10. P. P. Vergara, J. C. López, L. C. P. Silva, M. J. Rider, “Security- constrained optimal energy management system for three-phase residential microgrids”, Electric Power Systems Research, Volume 146, 2017, Pages 371-382, ISSN 0378-7796, 11. Heymann, B., Bonnans, J. F., Martinon, P. et al., “Continuous optimal control approaches to microgrid energy management”, Energy Syst 9, 59–77 (2018), https://doi.org/10.1007/s12667-016-0228-2 12. Luu Ngoc An and Tran Quoc-Tuan, "Optimal energy management for grid connected microgrid by using dynamic programming method," 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 2015, pp. 1-5, doi: 10.1109/PESGM.2015.7286094.
Editor's Notes
- #5: The main objective of ELD is to distribute the total power demand among the generating units by minimizing the selected cost criteria, subject to load and operation constraints. Conventional methods for solving ELD problem are: