Reverse Auction-based Demand Response Program: A Truthful Mutually Beneficial Mechanism
Reverse Auction-based Demand Response Program: A Truthful Mutually Beneficial Mechanism
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DOI：
10.1109/mass50613.2020.00059
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
A. R. Khamesi;S. Silvestri
中科院分区：
文献类型：
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作者：
A. R. Khamesi;S. Silvestri
在电力系统中，在高峰负荷时段匹配电力需求是众所周知的问题。事实上，当需求高时，由于需要启动备用发电机和加强输电系统，生产电力的成本会迅速增加。基于激励的需求响应（DR）计划是一种新的方法，智能电网技术的最新进展，旨在处理这样的问题。根据DR的说法，公用事业公司可以向用户提供经济激励，以便在高峰时段暂时减少他们的能源消耗。然而，确定分配这种激励的程序以及确保用户充分参与并满意以使DR计划有效是具有挑战性的。在本文中，我们提出了一个反向拍卖机制，使基于激励的DR计划。我们制定的DR逆向拍卖作为一个整数线性规划（ILP）的问题，它集成了一个感知价值的实用程序，用户的电器感知模型，以及公用事业公司的财务目标。采用基于Vickrey-Clarke-格罗夫斯（VCG）的逆向拍卖机制，保证了拍卖的真实性和个体理性。由于VCG拍卖要求最优地解决NP-Hard ILP问题，我们提出了一个启发式算法RADAR（Reverse Auction DemAnd Response），并证明了RADAR的真实性。使用几个家庭的真实的功耗数据进行的广泛模拟表明，RADAR在降低需求峰值方面是有效的，同时在用户感知效用方面优于以前的解决方案。
Matching power demand during peak load hours is a well-known problem in power systems. In fact, the cost of producing electricity increases very rapidly when the demand is high, due to the need for starting backup generators and enhancing transmission system. Incentive-based Demand Response (DR) program is a new approach, enabled by recent advances in smart grid technologies, designed to deal with such problem. According to DR, the utility company can provide economical incentives to users in order to temporarily reduce their energy consumption during peak hours. It is, however, challenging to determine the procedure to distribute such incentives, as well as to ensure that users will be sufficiently engaged and satisfied to make the DR program effective. In this paper, we propose a reverse auction mechanism to enable an incentive-based DR program. We formulate the DR reverse auction as an integer linear programming (ILP) problem, which integrates a perceived-value utility, to model the user perception of electrical appliances, as well as the financial objectives of the utility company. We adopt a Vickrey-Clarke-Groves (VCG) based reverse auction mechanism to guarantee the truthfulness and individual rationality properties. Since the VCG auction requires to optimally solve the NP-Hard ILP problem, we propose a heuristic algorithm named Reverse Auction DemAnd Response (RADAR), and prove that RADAR preserves truthfulness. Extensive simulations using real power consumption data of several homes show that RADAR is effective in reducing demand peaks while outperforming previous solutions in terms of users’ perceived utility.