On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

Bruninx, Kenneth; Pandžić, Hrvoje; Cadre, Hélène Le; Delarue, Erik

doi:10.1109/tpwrs.2019.2943670

Cited by 113 publications

(45 citation statements)

References 36 publications

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“…The provision of the ASs caused deviation from the reference operation points of aggregated resources, hence, the values of the objective function for all scenarios were lower compared to the reference case. The difference between the obtained value of the objective function (for Scenarios [1][2][3][4][5][6] and reference value should be treated as the minimum price of the offer for AS provision (Figure 11). The lowest profits were obtained in Scenarios 2, 4 and 6 in which RES curtailment was allowed, and a significant decrease in incomes due to lost generation occurred.…”

Section: Resultsmentioning

confidence: 99%

“…Small ESSs for household application, due to their growing number, can be aggregated The small size of distributed assets is a problem causing a lack of ability to participate in wholesale markets such as balancing markets and AS markets; moreover, legal obligations constitute a barrier for some entities. Therefore, an additional agent, that is, an aggregator, is required [1][2][3][4]. Figure 1 depicts the DRA coordinator as an entity responsible for distributed asset coordination and control enabling active participation of DERs in the management of power system operation.…”

Section: Introductionmentioning

confidence: 99%

“…Another study describes demand-side resources utilized for load scheduling to minimize the total cost of electricity procurement [20]. The high potential of aggregated flexibility, especially loads located in the residential sector, should be developed as a replacement for fossil fuel power plants' contribution in the continuous demand and supply balancing [4,21]. The need for peak-to-average demand reduction is addressed in [22], where the authors by the use of demand-side response reduce electricity charges for end-users and improve the shape of the load profile.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Distributed Resource Aggregation for the Provision of Ancillary Services

Lesniak¹,

Chudy²,

Dzikowski³

2020

Energies

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Nowadays, ancillary services (ASs) are usually provided by large power generating units located in transmission networks, while smaller assets connected to distribution systems remain passive. It is expected that active distribution systems will start to play an important role due to numerous issues related to power system operation caused mainly by developing renewable generation and restrictions imposed on conventional power generating units by climate policies. The future development of the power system management will also lead to the establishment of new market agents such as distributed resource aggregators (DRAs). The article presents the concept of the DRA as part of an active distribution system enabling small resources to participate in wholesale markets, provide ASs and indicates the functions of the DRA coordinator in the modern power system. The proposed method of the DRA structure modelling with the use of the mixed-integer linear programming (MILP) is aimed at evaluating the optimal operation pattern of participating resources, the desired shape of the load profile at the point of common coupling (PCC) and the AS provision. The performed simulations of the DRA’s operation show that various types of aggregated resources located in distribution networks are able to provide different services effectively to support the power system in terms of load–generation balancing and allow for further development of renewables.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling of Distributed Resource Aggregation for the Provision of Ancillary Services

Lesniak¹,

Chudy²,

Dzikowski³

2020

Energies

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“…The LA simulates the response mode of residential consumers and estimates response potential and response willingness of residents. On this basis, the LA formulates the optimal bidding strategies with the largest net profit and participate in this bidding [28,29]. This section will build the LA bidding model and propose a method to formulate the optimal bidding strategy.…”

Section: La Bidding Modelmentioning

confidence: 99%

Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

Song

Shi

et al. 2020

Applied Sciences

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As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

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“…For mathematical modeling, reference [9] provided a comprehensive mathematical model and calculation method for market clearing to study the unregulated market perspective in integrated energy systems. Reference [10] improved the optimal heat flow model and optimal power flow model for market clearing. Reference [11] proposed a two-stage hybrid random information gap decision theory model for the market clearing.…”

Section: Introductionmentioning

confidence: 99%

Transactive Energy-Based Joint Optimization of Energy and Flexible Reserve for Integrated Electric-Heat Systems

Zhao

Diao

et al. 2021

IEEE Access

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Implementing integrated electric-heat systems (IEHSs) with coupled power distribution networks and district heating networks is an essential means to solve current energy problems. However, prosumers with multiple energy forms coupled and renewable energy sources with natural uncertainties pose challenges to the operation of IEHSs. This paper proposes a joint energy and reserve dispatch model for IEHSs based on transactive energy, which is a coordinated combination of a bi-level Stackelberg game and two-stage robust optimization. The bi-level Stackelberg game is used to realize the equilibrium of interests among three transacting parties, namely, integrated energy service provider (IESP), multi-carrier prosumer (MCP), and load aggregator (LA). The two-stage robust optimization is employed to ensure the reliability of the system operation under renewable energy uncertainty. In the upper level of the Stackelberg game, the IESP perform pricing and reserve dispatch, while the MCP and LA maximize their benefits via energy management in the lower level. Linearization techniques are utilized to approximate the bi-level Stackelberg game model into a single-level mixed-integer linear programming problem. The converted single-level game model is subsequently regarded as the first stage, while the real-time feasibility check is regarded as the second stage to form a two-stage robust optimization model, which is solved by a modified C&CG algorithm. Case studies demonstrate that the proposed joint energy and reserve dispatch method effectively achieves economic and reliable operation. INDEX TERMS Integrated electric-heat system, transactive energy, joint energy and reserve dispatch, Stackelberg game, two-stage robust optimization.

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On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

Cited by 113 publications

References 36 publications

Modelling of Distributed Resource Aggregation for the Provision of Ancillary Services

Modelling of Distributed Resource Aggregation for the Provision of Ancillary Services

Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

Transactive Energy-Based Joint Optimization of Energy and Flexible Reserve for Integrated Electric-Heat Systems

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