Distributed generation planning from the investor's viewpoint considering pool-based electricity markets

Abstract Independent investors can install distributed generations (DGs) in power networks as long as they follow the systems’ rules and frameworks. Logically, they try to maximize their profits, and network owners/operators usually cannot directly control their decisions regarding the DG location and size. However, there are inherent interactions between their decisions and the systems’ technical and economic characteristics. In this paper, a new approach is presented for optimal DG placement and sizing from the standpoint of independent investors, with specific consideration given to interactions with pool-based electricity markets. The proposed approach is founded on the innovative combination of the meta-heuristic optimization model and the reinforcement learning-based market simulation strategy. This approach is implemented using the particle swarm optimization algorithm and the Q-learning technique, and it is comprehensively examined in the IEEE 30-bus system with consideration for different pricing rules and DG types. The results confirm that there are significant interdependencies between the optimal decision of DG investors and the strategic bidding behavior of market players. Also, they indicate that the proposed approach is useful for independent investors in identifying the most valuable DG location and size as well as for the systems’ policy-makers in investigating the long-term effects of specific rules.

[1]  Debapriya Das,et al.  A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses , 2020 .

[2]  Mahdi Pourakbari-Kasmaei,et al.  An economic-environmental asset planning in electric distribution networks considering carbon emission trading and demand response , 2020 .

[3]  I. Elamvazuthi,et al.  A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks , 2020, Appl. Soft Comput..

[4]  Saeed Tavakoli,et al.  Reliability based optimal allocation of distributed generations in transmission systems under demand response program , 2019, Electric Power Systems Research.

[5]  Nadarajah Mithulananthan,et al.  Optimal DG placement in deregulated electricity market , 2007 .

[6]  Heidar Ali Shayanfar,et al.  The role of demand response in optimal sizing and siting of distribution energy resources in distribution network with time-varying load: An analytical approach , 2020 .

[7]  Mohammad Farshad.  Multiagent-based simulation of simultaneous electricity market auctions in restructured environment , 2015 .

[8]  Simon Haykin,et al.  A dynamic channel assignment policy through Q-learning , 1999, IEEE Trans. Neural Networks.

[9]  H. A. Hejazi,et al.  Independent distributed generation planning to profit both utility and DG investors , 2013, IEEE Transactions on Power Systems.

[10]  Steve Isser,et al.  Electricity Restructuring in the United States: Markets and Policy from the 1978 Energy Act to the Present , 2015 .

[11]  Javier Contreras,et al.  Location and contract pricing of distributed generation using a genetic algorithm , 2012 .

[12]  J. I. Muñoz-Hernández,et al.  Uncertainty management of renewable distributed generation , 2016 .

[13]  Javad Sadeh,et al.  Analysis of Interaction Between Energy-spinning Reserve and Replacement Reserve Markets , 2011 .

[14]  G. Joos,et al.  Customer-Owned Back-Up Generators for Energy Management by Distribution Utilities , 2007, IEEE Transactions on Power Systems.

[15]  A. K. Singh,et al.  A review on distributed generation allocation and planning in deregulated electricity market , 2018 .

[16]  Mohammad Reza Hesamzadeh,et al.  The Economics of Electricity Markets , 2014 .

[17]  Nikos D. Hatziargyriou,et al.  Optimal Distributed Generation Placement in Power Distribution Networks : Models , Methods , and Future Research , 2013 .

[18]  Bindeshwar Singh,et al.  A review on distributed generation planning , 2017 .

[19]  Amir Ameli,et al.  A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner's and Distribution Company's Viewpoints , 2014, IEEE Transactions on Power Delivery.

[20]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[21]  S. Buonanno,et al.  Economic assessment of the installation of natural gas-fuelled microturbines and results of preliminary tests , 2001 .

[22]  Javad Sadeh,et al.  Encouraging Market Rule for DG Units in Pay-as-Bid Systems and a Novel DG Placement and Sizing Algorithm , 2012 .

[23]  Omid Bozorg-Haddad,et al.  Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization , 2017 .

[24]  Mohammad Reza Mohammadi,et al.  Optimal placement of multitypes DG as independent private sector under pool/hybrid power market using GA-based Tabu Search method , 2013 .

[25]  S. Okuma,et al.  Multi-agent based experiments on uniform price and pay-as-bid electricity auction markets , 2004, 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings.

[26]  Morad Mohamed Abdelmageed Abdelaziz,et al.  Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL , 2019, Electric Power Systems Research.

[27]  Marcos J. Rider,et al.  Bilevel approach for optimal location and contract pricing of distributed generation in radial distribution systems using mixed-integer linear programming , 2013 .

[28]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[29]  Matti Lehtonen,et al.  Optimal location-allocation of storage devices and renewable-based DG in distribution systems , 2019, Electric Power Systems Research.

[30]  Daniel M. Kammen,et al.  Fuel cell system economics: comparing the costs of generating power with stationary and motor vehicle PEM fuel cell systems , 2004 .

[31]  Mehdi Ehsan,et al.  Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment , 2011 .

[32]  H. Rajabi Mashhadi,et al.  A NOVEL TECHNIQUE FOR JOINT ENERGY AND RESERVE DISPATCH CONSIDERING LOST OPPORTUNITY COST , 2013 .

[33]  Salah Kamel,et al.  Efficient optimization technique for multiple DG allocation in distribution networks , 2020, Appl. Soft Comput..

[34]  Jeremy Lin,et al.  Electricity Markets: Theories and Applications: Theories and Applications , 2017 .

[35]  Ricardo Rüther,et al.  The influence of distributed generation penetration levels on energy markets , 2013 .

[36]  G. Joós,et al.  Models for Quantifying the Economic Benefits of Distributed Generation , 2008, IEEE Transactions on Power Systems.

[37]  Ali Ehsan,et al.  Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques , 2018 .

[38]  Miadreza Shafie-Khah,et al.  A Decentralized Renewable Generation Management and Demand Response in Power Distribution Networks , 2018, IEEE Transactions on Sustainable Energy.

[39]  Athanasios Dagoumas.  Impact of Bilateral Contracts on Wholesale Electricity Markets: In a Case Where a Market Participant Has Dominant Position , 2019 .

[40]  Mahmoud Pesaran H.A,et al.  A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms , 2017 .

[41]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..