Economic optimization for distributed energy network with cooperative game

Gao, Bingtuan; Liu, Xiaofeng; Chen, Chen; Li, Yuanmei

doi:10.1063/1.5034080

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The difference in energy demand characteristic is considered among 3 types of consumers, that is, the high electric demand of industrial consumers, the low energy demand of commercial buildings, and the obvious peak‐valley difference of residential communities. Accordingly, initial energy demand of consumers is shown in Figure 4 32 . Furthermore, assume that energy demand participating in DR can be shifted to three hours immediately preceding and following each hour.…”

Section: Case Studymentioning

confidence: 99%

A stackelberg game approach for heterogeneous energy market in integrated energy system

2020

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With the increasing demand of energy and the growing intensity of energy crisis, various smart energy systems are developed on the distribution level, such as integrated energy system (IES). In this article, a trading mechanism among various energy retailers and consumers is designed under the open market environment. The trading problem is formulated as a multi‐leader and multi‐follower Stackelberg game, which is built with the strategy set of energy bidding price and purchasing pattern. In particular, consumer's demand is described with the price elasticity property of demand, and non‐cooperative competition behavior among retailers is analyzed mathematically. To implement the proposed game approach, a distributed algorithm is presented by combining particle swarm optimization (PSO) and interior point method (IPM). Finally, simulation results show that the proposed trading mechanism will contribute to the development of heterogeneous energy market and the overall profit of retailers in the traditional trading mechanism is 37.8% lower than the profit in the proposed mechanism.

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Section: Case Studymentioning

confidence: 99%

A stackelberg game approach for heterogeneous energy market in integrated energy system

2020

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“…When consumers irregularly use electrical heating appliance for thermal demand, gas boiler can replace heating device to satisfy the same demand of users, thus reducing breach amount of DR resource. Assume that the breach amount of thermal demand in community n is h t n in time slot t, hence, following equation has to be obeyed [27]…”

Section: Gas Boiler Modelmentioning

confidence: 99%

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

Liu

Gao

2019

Applied Sciences

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Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users' breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource's characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.

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“…Game theory has been studied and applied in many fields, such as power grid vulnerability analysis [21], power plant pricing [22], and profit distribution [23,24]. In the field of integrated energy systems, a cooperative game model is established to determine the optimal scheduling strategy of the integrated energy system, aiming at the daytime economic optimal scheduling problem of the system [25]. Moreover, an energy grand coalition is constructed using a cooperative game, which can effectively reduce the variability in the local network load and minimize the running energy cost [26].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

et al. 2019

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The optimal scheduling of multi-energy hub systems plays an important role in the safety, stability, and economic operation of the system. However, due to the strong uncertainty of renewable energy access, serious coupling, and the interaction among energy hubs of multi-energy hub systems, it is difficult for the traditional optimal scheduling method to solve these problems. Therefore, game theory was used to solve the optimal scheduling problem of multi-energy hub systems. According to the internal connection mode and energy conversion relationship of energy hubs, along with the competitive and cooperative relationship between multi-energy hubs, the game theoretic optimal scheduling model of the multi-energy hub system was established. Then, two cases and 50 groups of wind speed series were used to test the robustness of the proposed method. Simulation results show that the total power injection is −16,805.8, 104.1847, and −865.561 and the natural gas injection is 46,046.81, 27,727.65, and 63,039.54 in spring/autumn, summer, and winter, respectively, which is consistent with the characteristics of the four seasons. Furthermore, the optimal scheduling method using game theory has a strong robustness in multi-energy hub systems.

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Economic optimization for distributed energy network with cooperative game

Cited by 6 publications

References 30 publications

A stackelberg game approach for heterogeneous energy market in integrated energy system

A stackelberg game approach for heterogeneous energy market in integrated energy system

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

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