Evaluation of Economic Benefits of DER Aggregation

Asimakopoulou, Georgia; Hatziargyriou, Nikos D.

doi:10.1109/tste.2017.2743349

Cited by 50 publications

(27 citation statements)

References 21 publications

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“…Our proof (which can be found in the Appendix) leverages a result from [21] that guarantees existence of a symmetric Nash equilibrium of the game among prosumers, given A's price. 4 Exploiting the properties of the penalty sharing mechanism, we further establish that there exists a unique symmetric Nash equilibrium x * (ρ) that varies smoothly with ρ. A will never opt for a price higher than λ DA and her profit varies smoothly in ρ ∈ [0, λ DA ].…”

Section: Figurementioning

confidence: 87%

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Quantifying Market Efficiency Impacts of Aggregated Distributed Energy Resources

Alshehri

Ndrio

Bose

et al. 2020

IEEE Trans. Power Syst.

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This paper focuses on the aggregation of distributed energy resources (DERs) through a profit-maximizing intermediary that enables participation of DERs in wholesale electricity markets. Particularly, we study the market efficiency brought in by the large-scale deployment of DERs and explore to what extent such benefits are offset by the profit-maximizing nature of the aggregator. We deploy a game-theoretic framework to study the strategic interactions between agreggators and DER owners. The proposed model explicitly takes into account the stochastic nature of the DER supply. We explicitly characterize the equilibrium of the game under general assumptions and provide illustrative examples to quantify the efficiency loss due to the strategic incentives of the aggregator. Our numerical experiments illustrate the impact of uncertainty and amount of DER integration on the overall market efficiency. * Authors are with the

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Section: Figurementioning

confidence: 87%

“…The smooth profit attains a maximum over that interval, leading to existence of a Stackelberg equilibrium. The relation in (4) implies that A's profit becomes strictly concave and hence, the maximum and the equilibrium become unique.…”

Section: Figurementioning

confidence: 99%

Quantifying Market Efficiency Impacts of Aggregated Distributed Energy Resources

Alshehri

Ndrio

Bose

et al. 2020

IEEE Trans. Power Syst.

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“…From another viewpoint, problems due to DERs propagate to these aggregators via DSO, and then TSO (Pudjianto et al, 2017). The idea in this paper is block this chain to DSO level since aggregators exist at DSO level too (Asimakopoulou and Hatziargyriou, 2018;Minniti et al, 2018). It requires a platform for aggregator at DSO level, with the integration, monitoring, and control capabilities using different market players (Han et al, 2017;Yazdani-Damavandi et al, 2017;Ju et al, 2018;Pasetti et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Distribution Level Aggregator Platform for DSO Support—Integration of Storage, Demand Response, and Renewables

2019

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Modern era is full of renewable energy resources, particularly wind and solar that are installed at both Low and Medium voltage nodes of the distribution system. With the diffusion of new resources in the distribution grid, a new challenge for the system operators is to cope up with the introduced changes in the distribution network. These changes can be on voltage levels, magnitudes and direction of current, and changes in power factor. The reduced visibility of these additives to operators require provision of ancillary services to ensure power system reliability and stability. In order to give flexibility to operator, and in particular DSO for distribution grid, the paper suggests the use of distribution level aggregators to serve as sub-DSO (JDSO). These aggregators utilize the three main components: Electrical energy storage system, customers demand response, and the renewable resources (PV and wind). These aggregators utilize a platform where all these actors can participate. DSO-JDSOs interaction is possible through the platform, and the local problems at distribution can be resolved before it goes to TSO. Paper presents and simulates the platform, develop test cases, and validate the model.

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“…In the study of distributed generation capacity selection, many studies have been carried out as follows. Reference [4] proposed a distributed energy resource aggregator technique to quantify the benefits of large-scale distributed generation access to the distribution network. Reference [5] proposed a planning model for multi-energy systems that considered gas pipelines and distribution networks, and used the minimum total investment cost as the optimization goal to conduct multi-stage planning and multi-scenario analysis of the model.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [5] proposed a planning model for multi-energy systems that considered gas pipelines and distribution networks, and used the minimum total investment cost as the optimization goal to conduct multi-stage planning and multi-scenario analysis of the model. However, this research mainly focused on economic factors [4,5] and did not consider the impact of the distributed generation capacity and location changes on the network. Reference [6] proposed a model considering the amplitude of the node voltage and the variation of wind speed, to solve the layout problem of wind power generation in the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

Jingjing

2019

Applied Sciences

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The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.

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Evaluation of Economic Benefits of DER Aggregation

Cited by 50 publications

References 21 publications

Quantifying Market Efficiency Impacts of Aggregated Distributed Energy Resources

Quantifying Market Efficiency Impacts of Aggregated Distributed Energy Resources

Distribution Level Aggregator Platform for DSO Support—Integration of Storage, Demand Response, and Renewables

Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

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