Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant

Pourghaderi, Niloofar; Fotuhi‐Firuzabad, Mahmud; Moeini‐Aghtaie, Moein; Kabirifar, Milad

doi:10.1109/tii.2018.2828039

Cited by 84 publications

(45 citation statements)

References 23 publications

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“…VPP can be categorized as commercial VPP (CVPP) and technical VPP (TVPP). Constraints of electricity grids in TVPPs' energy management strategy is considered but not in CVPP [46]. These VPPs use a bidding strategy considering their resources and market situation in, for example, day-ahead electricity market [46].…”

Section: Demand Response Aggregatormentioning

confidence: 99%

“…Constraints of electricity grids in TVPPs' energy management strategy is considered but not in CVPP [46]. These VPPs use a bidding strategy considering their resources and market situation in, for example, day-ahead electricity market [46]. In addition, energy storage integrated VPPs can contribute to the adequacy and reliability of generation systems in distribution networks [47,48], for which an optimization algorithm should determine the charging/discharging of energy storage.…”

Section: Demand Response Aggregatormentioning

confidence: 99%

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Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

et al. 2018

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The world is progressing towards a more advanced society where end-consumers have access to local renewable-based generation and advanced forms of information and technology. Hence, it is in a current state of transition between the traditional approach to power generation and distribution, where end-consumers of electricity have typically been inactive in their involvement with energy markets and a new approach that integrates their active participation. This new approach includes the use of distributed energy resources (DER) such as renewable-based generation and demand response (DR), which are being rapidly adopted by end-consumers where incentives are strong. This paper presents the role of the DR aggregator to effectively integrate DER technology as a new source of energy capacity into electricity networks using information communication technology and industry knowledge., Based on DR aggregators, this framework will efficiently facilitate renewable energy integration and customer engagement into the electricity market. To this aim, advantages and disadvantages of DR aggregators are discussed in this paper from political, economic, social, and technological (PEST) points of view. Based on this analysis, a strengths, weaknesses, opportunities, and threats (SWOT) analysis for a typical DR aggregator is presented.

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Section: Demand Response Aggregatormentioning

confidence: 99%

Section: Demand Response Aggregatormentioning

confidence: 99%

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

et al. 2018

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“…While in [14], an optimization model was proposed for planning and operating DR resource managed by a price-maker aggregator participating in the market, through both a deterministic and stochastic approach. Additionally, some examples on the bidding strategies of virtual power plants consisting of distributed generation and energy storage were also found in the literature [15][16][17][18]. Concretely, study [15] concentrated on a novel DR scheme that avoids the need to predict the price elasticity of demand by ranking candidate load profiles of consumers in the preference order.…”

Section: Introductionmentioning

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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“…On one hand, centralised systems have been traditionally used for the control and optimisation of power systems. In this type of implementation, management is carried out by applying mathematical optimisation techniques, such as linear programming [7,8], mixed-integer linear programming [9,10], and mixed-integer nonlinear programming [11], among other approaches. In addition, heuristic optimisation techniques, such as the gravitational search algorithm, particle swarm optimisation, and the genetic algorithm [12], have also been suggested.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm

2019

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This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation.

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Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant

Cited by 84 publications

References 23 publications

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

Aggregator of Demand Response for Renewable Integration and Customer Engagement: Strengths, Weaknesses, Opportunities, and Threats

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

Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm

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