Day-Ahead Market Optimal Bidding Strategy and Quantitative Compensation Mechanism Design for Load Aggregator Engaging Demand Response

Wang, Fei; Ge, Xinxin; Li, Kangping; Mi, Zengqiang

doi:10.1109/tia.2019.2936183

Cited by 106 publications

(27 citation statements)

References 42 publications

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“…In addition, this paper only considers price-based DR programs [38], [39] such as TOU [40]. Actually as another important DR program, incentive-based DR program [41] is gradually applied into the residential sector [42]. The optimal scheduling of appliance under incentive-based DR programs will be incorporated into the HEMS in the future.…”

Section: Discussionmentioning

confidence: 99%

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the increasing popularity of natural gas appliances in modern smart homes, the electricity consumption of residential customers connected to the natural gas network is significantly affected by the use of natural gas appliances. To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed. Firstly, all household appliances are classified into several categories and the mathematical model of each appliance is presented. Then, a day-ahead optimal joint scheduling model of both electric and natural gas appliances for HEMS is formulated, in which the objective function is to minimize the household's energy cost and the dissatisfaction level caused by the shifting, reduction and replacement of loads in response to the time varying prices. Case studies using realistic data indicate that the proposed model can save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels. INDEX TERMS Home energy management system, natural gas, demand response, joint scheduling, dissatisfaction. A. SUPERSCRIPTS bch Battery charging bdch Battery discharging dw Dishwasher eac Electric air conditioner el Electric light es Electric stove ev Electric vehicle ewh Electric water heater gahs Gas air heating system The associate editor coordinating the review of this manuscript and approving it for publication was Alexander Micallef. gs Gas stove gwh Gas water heater ua Uncontrollable appliance s Stove wh Water heater wm Washing machine B. INDEXES AND SETS

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Section: Discussionmentioning

confidence: 99%

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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“…Likewise, just industrial loads are studied in [13] and [14] without considering other types of consumers. Several models only considered the uncertainty of the electricity market for DR frameworks [15], [16]. For instance, Abapour et al [15] proposed robust scheduling for a DR aggregator through game theory by the price uncertainty assumption.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…For instance, Abapour et al [15] proposed robust scheduling for a DR aggregator through game theory by the price uncertainty assumption. Moreover, Wang et al [16] formulated an optimal bidding strategy for an aggregator. The electricity price of the day-ahead market is managed as the risk factor.…”

Section: B Literature Reviewmentioning

confidence: 99%

Novel Hybrid Stochastic-Robust Optimal Trading Strategy for a Demand Response Aggregator in the Wholesale Electricity Market

Vahid‐Ghavidel

Javadi

Santos

et al. 2021

IEEE Trans. on Ind. Applicat.

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The close interaction between the electricity market and the end-users can assist the demand response (DR) aggregator in handling and managing various uncertain parameters simultaneously to reduce their effect on the aggregator's operation. As the DR aggregator's main responsibility is to aggregate the obtained DR from individual consumers and trade it into the wholesale market. Another responsibility of the aggregator is proposing the DR programs (DRPs) to the end-users. This article proposes a model to handle these uncertainties through the development of a novel hybrid stochastic-robust optimization approach that incorporates the uncertainties around wholesale market prices and the participation rate of consumers. The behavior of the consumers engaging in DRPs is addressed through stochastic programming. Additionally, the volatility of the electricity market prices is modeled through a robust optimization method. Two DRPs are considered in this model to include both time-based and incentive-based DRPs, i.e., time-of-use and incentive-based DR program to study three sectors of consumers, namely industrial, commercial, and residential consumers. An energy storage system is also assumed to be operated by the aggregator to maximize its profit. The proposed mixed-integer linear hybrid stochastic-robust model improves the evaluation of DR aggregator's scheduling for the probable worst-case scenario.

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“…Then, each load category is scheduled for distribution by grouping aggregates to maximize the benefits in the electricity market. In [41], using a mixed integer linear programming problem, the authors propose an optimal bidding strategy model for a DR operator to reduce the risk of financial loss caused by price volatility. In [42], the authors propose a selfscheduling framework for DR operators to consider the uncertainties posed by customers and electricity market prices.…”

Section: ) Optimal Bidding and Coordination Strategymentioning

confidence: 99%

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

Lee

Jang²,

et al. 2021

IEEE Access

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Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer's data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

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Day-Ahead Market Optimal Bidding Strategy and Quantitative Compensation Mechanism Design for Load Aggregator Engaging Demand Response

Cited by 106 publications

References 42 publications

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Novel Hybrid Stochastic-Robust Optimal Trading Strategy for a Demand Response Aggregator in the Wholesale Electricity Market

Novel Single Group-Based Indirect Customer Baseline Load Calculation Method for Residential Demand Response

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