Demand Side Management in Smart Grids: A Stackelberg Multi Period Multi Provider Game

Alsalloum, Hala; ELMasri, Ali; Merghem-Boulahia, Leïla; Rahim, Rana

doi:10.1109/ntms.2018.8328687

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…In order to find the equivalent single level of this bi-level problem, first, we need to get rid of the non-convexities, and a straightforward way is to use some appropriate linearization techniques. The absolute term can be easily linearized using two positive auxiliary variables as in (23)- (24) and 32 7, special ordered sets of type two (SOS2) variables are employed as in (25)- (31). SOS2 is a set of variables in which not more than two variables take nonzero values and these variables are adjacent to each other.…”

Section: Optimization Formulationmentioning

confidence: 99%

“…The problem was solved iteratively until achieving an equilibrium point. Similarly, a real-time pricing strategy based on the Stackelberg game was proposed in [24] in which equilibrium was obtained by trading energy between prosumers and consumers aiming to maximize the prosumer's profit and minimize the consumer's bill. The interaction between the regional control center and village microgrids was studied in [25] to achieve Stackelberg equilibrium.…”

Section: Introductionmentioning

confidence: 99%

“…In the above reformulation (45)-(63), the absolute value functions will be substituted with their corresponding auxiliary variables presented in (23)- (24) and (32)- (33). The equations (47)-(48), (49)-(50), (51)-(52), (53)-(54), (55)-(56), (57) and (58) are the dual constraints corresponding to the primal variables…”

mentioning

confidence: 99%

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Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

et al. 2020

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This paper proposes a Stackelberg game approach to minimize both the wind power imbalances and carbon emissions by harnessing demand response (DR) of residential heating loads fed by electricity or district heating (DH) options. The problem is formulated as a bilevel optimization model. An aggregator (leader) at the upper level aims at minimizing the mismatch between electricity demand and wind power while mitigating the carbon emissions arising from the DH system. The aggregator owns a wind farm and is responsible for controlling the DH generation through a combustion-based source and a deep well heat pump system (DWHP), which converts power into heat by utilizing wind power and replacing combustion-based DH. The aggregator submits bonuses to the households (followers) at the lower level incentivizing them to modify their consumption profile. The households receive the bonus offers and consequently decide how to optimize their net electricity and DH energy payments via an upward or downward DR strategy. The uncertainties associated with wind power and heating loads are considered using a stochastic programming framework. Long term thermal performance of the DWHP is studied separately. Results prove that the proposed bilevel framework enables significant reductions in wind power imbalances, carbon emissions, and energy payments. INDEX TERMS Stackelberg game, demand response, district heating, deep well heat pump, bilevel optimization, power to heat, sector coupling

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Section: Optimization Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

et al. 2020

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“…In the absence of competition, we used an exogenous constraint (Equation ( 29)) to limit the negative impacts on consumer welfare and efficiency losses. The expansion of this model to a multi-leader and multi-follower (such as the model in [64]) can tackle this limitation of our work in neglecting the competition among different retailers. Moreover, we did not consider any collusive behavior among the households in our lower-level problem.…”

Section: Limitations and Outlookmentioning

confidence: 99%

Aggregation of Households in Community Energy Systems: An Analysis from Actors’ and Market Perspectives

2020

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In decentralized energy systems, electricity generated and flexibility offered by households can be organized in the form of community energy systems. Business models, which enable this aggregation at the community level, will impact on the involved actors and the electricity market. For the case of Germany, in this paper different aggregation scenarios are analyzed from the perspective of actors and the market. The main components in these scenarios are the Community Energy Storage (CES) technology, the electricity tariff structure, and the aggregation goal. For this evaluation, a bottom-up community energy system model is presented, in which the households and retailer are the key actors. In our model, we distinguish between the households with inflexible electricity load and the flexible households that own a heat pump or Photovoltaic (PV) storage systems. By using a game-theoretic approach and modeling the interaction between the retailer and households as a Stackelberg game, a community real-time pricing structure is derived. To find the solution of the modeled Stackelberg game, a genetic algorithm is implemented. To analyze the impact of the aggregation scenarios on the electricity market, a “Market Alignment Indicator” is proposed. The results show that under the considered regulatory framework, the deployment of a CES can increase the retailer’s operational profits while improving the alignment of the community energy system with the signals from the electricity market. Depending on the aggregation goal of the retailer, the implementation of community real-time pricing could lead to a similar impact. Moreover, such a tariff structure can lead to financial benefits for flexible households.

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“…In this game, the consumers are the players, and the strategies are the actions. A GT is presented in Reference 82 in which different consumers and providers interact with each other to get the best price. The heuristics methods as the genetic algorithms are useful also in this context to reduce consumers’ bills 83 .…”

Section: Optimization Techniques For Energy Tradingmentioning

confidence: 99%

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

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Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

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Demand Side Management in Smart Grids: A Stackelberg Multi Period Multi Provider Game

Cited by 8 publications

References 10 publications

Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

Aggregation of Households in Community Energy Systems: An Analysis from Actors’ and Market Perspectives

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

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