Game-theoretic energy management with storage capacity optimization in the smart grids

Gao, Bingtuan; Liu, Xiaofeng; Wei, Cheng; Tang, Yi

doi:10.1007/s40565-017-0364-2

Cited by 25 publications

(24 citation statements)

References 38 publications

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“…Pricing mechanism is a solution for on-peak load management; however, the scheduling mechanism is required to shift load in a suitable manner. To achieve this, works in [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] present different load controlling techniques and strategies. For instance, the work in [20] proposes a Distributed Energy Storage Planning (DESP) for PAR minimization.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, the real-time DR model is presented to minimize the electricity cost associated with consumption through appliance scheduling while ignoring consumer comfort. Another article [26] presents a model for residential users to minimize their consumption cost. It is assumed that all consumers are selfish and they are only concerned about minimizing their energy utilization cost.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Time-of-Use Electricity Price Rate Using Game Theory

et al. 2019

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The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhanced Time-of-Use Electricity Price Rate Using Game Theory

et al. 2019

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“…l n (k) represents energy utilization of n users that contains both shift able and non shift able load. Equations (1)- (6) have been taken from [45]. Daily energy consumption of load is explained with the help of equation below:…”

Section: Cost Model Of Usersmentioning

confidence: 99%

“…C k X k represents total cost of energy consumption by k th user. Whereas, total cost of N user is calculated as follows [45]:…”

mentioning

confidence: 99%

Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

et al. 2019

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In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques.

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“…Moreover, the DSO could make concession on its current profit to achieve the optimal longterm development plan. This approach is significantly superior to the previous studies focusing on the interests of consumers [29,30] or the profits of retailers [31], as those studies could not avoid falling into sub-optimum due to the short-sightedness of consumers/retailers.…”

Section: Introductionmentioning

confidence: 99%

Distributed generation planning for diversified participants in demand response to promote renewable energy integration

Dang

Wang

Shao

et al. 2019

J. Mod. Power Syst. Clean Energy

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In modern distribution system, the distribution system operator (DSO) acts as a market facilitator and data manager as well as an energy supplier and operation controller. In this circumstance, the DSO should comprehensively consider the diversified participants of the modern distribution system when making investment decisions of distributed generation (DG). This paper proposes a DG planning model considering the behavior of the diversified participants, which are motivated to cooperate with distributed renewable energy resources to promote their integration, and to achieve the optimal DG investment plan. The optimization model takes a centralized structure but fully considers the preferences, profits and comfort levels of the aggregators and consumers. The model is linearized into a mixed-integer linear programming (MILP) problem and is solved by CPLEX. Results of the case study show that when the DSO spares subsidies to the aggregators and consumers to encourage their participation in demand response (DR) programs, it earns more compared with providing no subsidies for DR participation. It is also demonstrated that the overall profit increases as the subsidies increase within a certain range, but decreases when the subsidies exceed this range. Therefore, the DSO needs to carefully choose the subsidization level to achieve the optimal utilization of renewable energy and demand flexibility. The optimal subsidization level is derived from the model proposed in this paper. Therefore, this paper puts forward a new pattern to utilize the distributed renewable energy sources, and provides guidance in policy making and DR program implementation.

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Game-theoretic energy management with storage capacity optimization in the smart grids

Cited by 25 publications

References 38 publications

Enhanced Time-of-Use Electricity Price Rate Using Game Theory

Enhanced Time-of-Use Electricity Price Rate Using Game Theory

Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid

Distributed generation planning for diversified participants in demand response to promote renewable energy integration

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