A Data-Driven Stackelberg Market Strategy for Demand Response-Enabled Distribution Systems

Lu, Tianguang; Wang, Zhaoyu; Wang, Jianhui; Ai, Qian; Wang, Chong

doi:10.1109/tsg.2018.2795007

Cited by 79 publications

(39 citation statements)

References 29 publications

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“…From the obtained results, the proposed model improved demand profile by 33% and 40% for hot and cold weather conditions, respectively, compared to the conventional energy management system. Similar research can be found in [20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Methodssupporting

confidence: 82%

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Essiet

Sun

2020

Applied Sciences

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This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

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Section: Methodssupporting

confidence: 82%

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Essiet

Sun

2020

Applied Sciences

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“…In this setting, each market participant solves an optimization problem to determine a bid which they submit to a facilitator, who in turn incorporates the bids in a market clearing optimization problem that determines prices and the consumption or production allocated to each bidder. In this context, the facilitator may impute bidders' right-hand-side constraint parameters, such as bounds on consumption, which can then be used to inform a pricing strategy that aims to maximize profit or control peak demand (Saez-Gallego et al, 2016;Saez-Gallego & Morales, 2018;Xu et al, 2018;Lu et al, 2019). Similarly, a bidder may seek to impute several unknown parameters which can be used in the process of deciding her bid.…”

Section: Motivating Applicationsmentioning

confidence: 99%

Inverse optimization for the recovery of constraint parameters

Chan

Kaw

2020

European Journal of Operational Research

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Most inverse optimization models impute unspecified parameters of an objective function to make an observed solution optimal for a given optimization problem with a fixed feasible set. We propose two approaches to impute unspecified left-hand-side constraint coefficients in addition to a cost vector for a given linear optimization problem. The first approach identifies parameters minimizing the duality gap, while the second minimally perturbs prior estimates of the unspecified parameters to satisfy strong duality, if it is possible to satisfy the optimality conditions exactly. We apply these two approaches to the general linear optimization problem. We also use them to impute unspecified parameters of the uncertainty set for robust linear optimization problems under interval and cardinality constrained uncertainty. Each inverse optimization model we propose is nonconvex, but we show that a globally optimal solution can be obtained either in closed form or by solving a linear number of linear or convex optimization problems.

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“…Neural networks played a significant role in balancing between the thermal comfort and energy use in buildings [28], achieving optimal dispatch in ancillary services market [29] and reduced load curtailments [16]. In [30], a datadriven model was used to increase the learning ability for price responsive behaviors. A game theory-based market strategy was designed to increase profit of each market participant with operational security guarantees.…”

Section: A Category 1 Boundary Parameter Improvementmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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A Data-Driven Stackelberg Market Strategy for Demand Response-Enabled Distribution Systems

Cited by 79 publications

References 29 publications

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Inverse optimization for the recovery of constraint parameters

Review of Learning-Assisted Power System Optimization

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