Optimal design of reward-penalty demand response programs in smart power grids

Ghorashi, Seyed Morteza; Rastegar, Mohammad; Senemmar, Soroush; Seifi, Ali Reza

doi:10.1016/j.scs.2020.102150

Cited by 25 publications

(10 citation statements)

References 31 publications

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“…If a consumer exceeds the contract, he should pay the penalty. This possibility was discussed in [37] which presents a rolling penalty function for real-time pricing of microgrids (see [37], [38] for more detail).…”

Section: ) Penalty-based Flexible Loadmentioning

confidence: 99%

Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach

et al. 2021

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This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system (MRES) including distributed energy resources (DERs). The main objective of the proposed scheduling approach is the minimization of the total costs of an MRES consisting of both electricity and gas energy carriers. The proposed model considers both electrical and natural gas distribution networks, DER technologies including renewable energy resources, energy storage systems (ESSs), and combined heat and power. The uncertainties pertinent to the demand and generated power of renewable resources are modeled using the chance-constrained approach. The proposed model is applied on the IEEE 33-bus distribution system and 14-node gas network, and the results demonstrate the efficacy of the proposed approach in the matters of diminishing the total operation costs and enhancing the reliability of the system.

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Section: ) Penalty-based Flexible Loadmentioning

confidence: 99%

Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach

et al. 2021

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“…For each prosumer u, the energy stored in the EV battery in each period t is determined in (12). In (13), the variation interval related to the charging time of the EV battery is established considering the limits [τ ev u , τ ev u ], while (14) ensures the number of times that the EV battery can be charged within the range Q ev u , Q ev u . The optimal profile, O cp u,a,t , of the EV (β a = −1) related to prosumer u is obtained by (15) as the total energy stored in the EV battery during the day is calculated using (16).…”

Section: Home Appliances Constraintsmentioning

confidence: 99%

“…As during off-peak hours the energy tariff is cheaper, many prosumers prefer to postpone or anticipate the usage of the appliance, then the usage of their appliances may coincide, especially those with higher average power, within these periods [12]. These events can affect the power grid performance, compromising the assets lifetime, mainly of the power transformers, feeders, and protection devices [13].…”

Section: Introduction 1overviewmentioning

confidence: 99%

Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement

et al. 2021

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In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.

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“…They verify that the established model can promote the enthusiasm of the user side to participate in IBDRM. Ghorashi et al [32] face the phenomenon that users' participation in IBDRM may lead to a peak rebound and the power transmission system congestion during low load periods, they study the design and optimization of demand response compensation mechanism by combining the smart grid technology. Moreover, the numerical analysis indicates that the proposed compensation mechanism can improve the operational characteristics of the power grid and reduce the peak rebound without increasing the costs of users.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis From the Perspective of Direct Power-Purchase for Industrial Users: Should the Power Grid Company Implement Incentive-Based Demand Response Management?

Wang

Jia

et al. 2021

IEEE Access

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As a means for the power grid company to guide industrial users to reduce power consumption through economic compensation, incentive-based demand response management (IBDRM) plays an important role in the process that industrial users purchase power directly from power plants (direct power-purchase for short). Therefore, this paper studies the impacts of IBDRM on the power grid company, power plants, and industrial users from the perspective of industrial users' direct power-purchase, so as to analyze whether the power grid company should implement IBDRM in direct power-purchase. First, we model the interactions of the power grid company, the power plant, and the industrial user under different scenarios, including Case N (the power grid company does not implement IBDRM) and Case R (the power grid company implements IBDRM) by using Stackelberg game. Then, we solve the models to get equilibrium results. The primary contributions of this article include following parts: 1) Compared with Case N, the power plant's power selling price and the industrial user's power-purchase quantity are higher under Case R, and both are negatively correlated with the compensation for the power plant. 2) The power grid company should implement IBDRM in direct power-purchase. Because under Case R, the industrial user reduces the power consumption, the profits of the power grid company and the power plant are higher, and the social welfare is also greater. 3) Under Case R, we also find that the social welfare is positively correlated with the compensation for the industrial user.

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Optimal design of reward-penalty demand response programs in smart power grids

Cited by 25 publications

References 31 publications

Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach

Network-Constrained Optimal Scheduling of Multi-Carrier Residential Energy Systems: A Chance-Constrained Approach

Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement

An Analysis From the Perspective of Direct Power-Purchase for Industrial Users: Should the Power Grid Company Implement Incentive-Based Demand Response Management?

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