Long-Term Planning of Connected Industrial Microgrids: A Game Theoretical Approach Including Daily Peer-to-Microgrid Exchanges

Stevanoni, Charline; Grève, Zacharie De; Vallée, François; Deblecker, Olivier

doi:10.1109/tsg.2018.2793311

Cited by 52 publications

(26 citation statements)

References 25 publications

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“…3) Peer to microgrid energy trading: An interesting concept of energy exchange between peer and microgrid is studied in [33]. The objective is the long-term planning of the peer connected industrial microgrids by coupling the decision of long-term investments and short-term operations via two game theoretical frameworks that allow the modeling of the different, even conflicting, objectives of the stakeholders.…”

Section: General Focus Of the Studymentioning

confidence: 99%

A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

et al. 2019

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Peer-to-peer trading in energy networks is expected to be exclusively conducted by the prosumers of the network with negligible influence from the grid. This raises the critical question: how can enough prosumers be encouraged to participate in peer-to-peer trading so as to make its operation sustainable and beneficial to the overall electricity network? To this end, this paper proposes how a motivational psychology framework can be used effectively to design peer-to-peer energy trading to increase user participation. To do so, first, the state-of-the-art of peer-to-peer energy trading literature is discussed by following a systematic classification, and gaps in existing studies are identified. Second, a motivation psychology framework is introduced, which consists of a number of motivational models that a prosumer needs to satisfy before being convinced to participate in energy trading. Third, a game-theoretic peer-to-peer energy trading scheme is developed, its relevant properties are studied, and it is shown that the coalition among different prosumers is a stable coalition. Fourth, through numerical case studies, it is shown that the proposed model can reduce carbon emissions by 18.38% and 9.82% in a single day in Summer and Winter respectively compared to a feed-in-tariff scheme. The proposed scheme is also shown to reduce the cost of energy up to 118 ¢ and 87 ¢ per day in Summer and Winter respectively. Finally, how the outcomes of the scheme satisfy all the motivational psychology models is discussed, which subsequently shows its potential to attract users to participate in energy trading.

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Section: General Focus Of the Studymentioning

confidence: 99%

A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

et al. 2019

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“…The fine-tuning effect determines the performance of the algorithms in achieving global or local optimum solutions [16]. Optimal energy management in industrial MGs has been considered [17][18][19], whereas residential MG optimal energy management is proposed in [20][21][22][23]. Due to the stochastic nature of RESs, deterministic approaches may not ensure the quality of power which an MG can generate.…”

Section: Energy Management Using Deterministic and Chance-constrainedmentioning

confidence: 99%

“…The computational steps of the Jaya optimization technique depend upon the complexity at lines . The complexity of other lines (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) are superseded by these lines; the complexity of line 13 is influenced by line 15. Sub-lines 15-23 take O(1); while line 15 can iterate maximally with k iterations, and thus its cost is O(k).…”

Section: Jaya Algorithmmentioning

confidence: 99%

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Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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“…Kaveh et al [15] proposed a hierarchical energy management model, in which the optimal solution is obtained through Nash bargaining at the lower level, where the utility company facilitates power exchange between multiple MGs and the wholesale market at the highest level. Stevanoni et al [16] proposed a tool for long-term planning of the connected industrial MGs, and the long-term investments and short-term decisions are coupled via the game approach. An energy trading framework based on the Stackelberg game theory was proposed to improve economic benefits of the MMG [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Energy Market Management for Distribution Network with a Multi-Microgrid System: A Dynamic Game Approach

Tong

Zheng

et al. 2019

Applied Sciences

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This paper proposes an energy market management framework to address the day-ahead optimization of a distribution network (DN) with a multi-microgrid (MMG). In this framework, an economic optimization based on a dynamic game is designed to ensure the reliability and stability of the DN operation while setting the internal transaction price to minimize the operation cost of the distribution network operator (DNO). The DNO, as a leader, sets the power flow constraints and microgrid operators (MGOs), as followers, respond to the internal transaction price and in turn affect the DNO's price decision. The price incentive mechanism of the DNO is to encourage MGs to participate in energy market management, to promote joint operation of MGs and to reduce the operation cost of MGOs. The differential evolution algorithm and mixed integer programming method are applied to find the equilibrium solution to the dynamic game. Numerical simulations of a modified IEEE 33-bus system show the effectiveness of the proposed energy market management framework.

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Long-Term Planning of Connected Industrial Microgrids: A Game Theoretical Approach Including Daily Peer-to-Microgrid Exchanges

Cited by 52 publications

References 25 publications

A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

A motivational game-theoretic approach for peer-to-peer energy trading in the smart grid

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

Energy Market Management for Distribution Network with a Multi-Microgrid System: A Dynamic Game Approach

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