Opportunities for Price Manipulation by Aggregators in Electricity Markets

Azizan, Navid; Chen, Niangjun; Dvijotham, Krishnamurthy; Wierman, Adam

doi:10.1145/3003977.3003995

Cited by 10 publications

(20 citation statements)

References 13 publications

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“…Several business models have been developed to maximize the profit of the aggregator. For example, strategic curtailment of generation in an electricity market was proposed in [26] by maximizing curtailment profit subject to locational marginal prices and curtailment constraints. A double-auction scheme has been widely used to maximize the social welfare [2].…”

Section: Aggregatormentioning

confidence: 99%

Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions

Chiu

2022

IEEE Systems Journal

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Owing to energy liberalization and increasing penetration of renewables, renewable energy trading among suppliers and users has gained much attention and created a new market. This article investigates a double-auction scheme operated by an aggregator with limited supervision for energy trading. To ensure beneficial bidding for renewable generators and end users (EUs) considered as agents, a multiagent Q-learning (MAQL) based bidding strategy is developed to maximize their cumulative reward. Each agent first provides their information about renewable supply or demand to an aggregator who will then return the information about aggregate supply and demand. Without knowing the business model of the aggregator, the agents use Q-tables to estimate the expected cumulative reward and determine their bidding prices accordingly. Finally, the aggregator coordinates energy trading between agents who will then update their Q-tables on the basis of the amount of power bought or sold at the prices they bid. The proposed approach can avoid some unnecessary or unrealistic assumptions generally made by model-based approaches, such as the assumption on the knowledge of others' bidding profiles or the assumption of an oligopoly; it can consider the influence of bidding strategies on the market, which cannot be properly addressed by a conventional proportional allocation mechanism. A numerical analysis using real-world data and considering a profit maximization model for the aggregator shows that the proposed approach outperformed comparable methods in terms of profits of renewable generators and energy satisfaction level of EUs: iterative double auction by approximately 29%, heuristic bidding by 39.9%, random bidding by 38.1%, NSGA-II-based multiobjective approach by 62%, and MOEA/D-based multiobjective approach by 83.1% on average.

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Section: Aggregatormentioning

confidence: 99%

Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions

Chiu

2022

IEEE Systems Journal

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“…DR benefits include, but not limited to, improving the economic efficiency of electricity markets, enhancing the reliability of electric power systems, reducing peak demand, and alleviating price volatility [1]. Various DR programs have been upheld by power system operators to promote the active contribution of demand-side entities and harvest associated benefits [2].…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, several objectives can be identified from different standpoints. The potential profits which can be attained by DR aggregation are quantified in [2]. From the end-user perspective, several objective functions are proposed which offer cost-effective and comfort aware DR programs [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform

Teimourzadeh¹,

Tör²,

Cebeci³

et al. 2020

IEEE Access

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The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers' flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the webbased user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies.

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“…Potential for aggregators to manipulate the electricity market price via strategic curtailment may exist in practical settings [32]. Such curtailments are performed through demand response services.…”

Section: Introductionmentioning

confidence: 99%

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

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We model the relation between an aggregator and consumers joining a coalition to reduce the risk resulting from the unpredictability of their base load demand, as a Stackelberg game formulated as a mathematical bilevel program with private information on the consumers' reservation prices. At the upper-level of the Stackelberg game, the aggregator optimizes his daily price profile so as to reach a net targeted profit which is the maximum value guaranteeing that no consumer will leave the coalition -to contract with a conventional retailer considered here as a fixed alternative -while meeting fairness criterion imposed by the cost-sharing mechanism. At the lower-level, the consumers are asked to provide in day ahead an estimate of their base load hourly demand profile and to schedule their shiftable loads depending on the price signal sent by the aggregator. We provide algorithms that determine the unique price profile and consumer shiftable load schedules as functions of the reservation price estimates. The Stackelberg game between the aggregator and the consumers being repeated for a period of time, the aggregator has the possibility to update his estimates of the reservation prices relying on a feedback function which depends on the percentage of activated loads. A randomized algorithm for consumers' reservation price learning based on regret minimization is provided. For four cost-sharing mechanisms such as uniform allocation, stand-alone cost, Shapley value, separable and non-separable costs, we determine the closed form of the aggregator's optimal net targeted profit guaranteeing the stability of the coalition. We also determine conditions guaranteeing the core non-emptiness and prove that for a profit-maximizing aggregator, the stand-alone cost is always preferable to the Shapley value, which coincides with the uniform allocation. Furthermore, the optimal size of the coalition -in terms of the aggregator's profit -can be determined analytically when the Shapley value is implemented as cost-sharing mechanism. The results are illustrated on a case study where we show that there exists an optimal net targeted profit below which the consumers energy bill is lower when joining the aggregator than with the conventional retailer. Coalition dynamics is also analyzed numerically depending on the consumer inertia in their energy supplier choice process, for each cost-sharing mechanism.

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Opportunities for Price Manipulation by Aggregators in Electricity Markets

Cited by 10 publications

References 13 publications

Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions

Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions

Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

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