Optimal Bidding of Price-Maker Retailers With Demand Price Quota Curves Under Price Uncertainty

Wang, Ying; Wang, Dehao; Zhang, Huaiyu; Zhang, Kaifeng

doi:10.1109/access.2020.3005932

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…Step: 8 Smoothing of mean and standard deviation of population As elite particles are near to sub-optimal solutions, more smoothing (weightage) is applied to their mean value as compared to mean of whole population as shown in Equation (15):…”

Section: Cross Entropy (Ce) Methodsmentioning

confidence: 99%

“…Step: 15 Fitness function evaluations and ranking The fitness of all particles is evaluated by using Equation ( 7) which calculates the profits/costs of all agents and the standard deviation between the profits/costs of agents. Sort (rank) all fitness values in ascending order and find G Best and top 50% elite particles (µ) which have better fitness than the remaining 50% particles.…”

Section: Cmaes Methodsmentioning

confidence: 99%

“…The work used a standard PSO, showing poor local exploitation search ability and, thus, suffering from premature convergence. The work in [15] also used a standard Genetic Algorithm (GA) to optimize the bidding price of retailers, considering probability-based estimation of market prices. The results showed that GA is quite computationally expensive, affecting the application capabilities of the approach.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

et al. 2022

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The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.

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Section: Cross Entropy (Ce) Methodsmentioning

confidence: 99%

Section: Cmaes Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

et al. 2022

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“…Self-production of energy was considered in [8]- [11], [15]- [18], and [26] while energy storage system and virtual bidding were only considered in [11] and [27], respectively. The determination of the retailer's optimal power bidding curves in the electricity market was considered in [8]- [14], [18], [20], [22], and [28]- [31]. Among all existing risk management tools for scenariobased decision-making models under uncertainty, CVaR has received increased attention in decision-making models for different electricity market participants such as generation companies, large consumers, and retailers [5].…”

Section: Randommentioning

confidence: 99%

A Decision Model for an Electricity Retailer With Energy Storage and Virtual Bidding Under Daily and Hourly CVaR Assessment

Prado

Chikezie

2021

IEEE Access

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This paper presents a short-term decision-making model for an electricity retailer with battery energy storage system (BESS) and virtual bidding through a two-stage stochastic optimization framework. In the first stage, the retailer determines the amount of power to be purchased in the day-ahead wholesale market and the optimal incremental and decremental virtual bidding strategies. In the second stage, the optimal energy storage decisions and the retailer's involvement in the real-time market are determined. The proposed model minimizes the retailer's expected procurement cost and generates the optimal power and virtual bidding curves in the day-ahead market. Two types of Conditional Value at Risk (CVaR) are integrated in the proposed model to manage the retailer's hourly and daily risks, respectively. Case studies with real-world data are performed to verify the retailer's cost reduction obtained with the integration of BESS and virtual bidding and to study how the hourly and daily risk-management strategies affect the retailer's procurement cost distribution for different risk-aversion levels.

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“…In this, a basic PSO is implemented whose local exploitation search ability is poor and thus suffers with pre-mature convergence. Authors of [10] used basic Genetic Algorithm (GA) to optimize the bidding of price maker retailers with probability-based estimation of market price. But the GA is quite computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Cross Entropy Covariance Matrix Adaptation Evolution Strategy Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets with Renewables

Dabhi¹,

Pandya²,

Soares³

et al. 2021

Preprint

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The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

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Optimal Bidding of Price-Maker Retailers With Demand Price Quota Curves Under Price Uncertainty

Cited by 11 publications

References 27 publications

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

A Decision Model for an Electricity Retailer With Energy Storage and Virtual Bidding Under Daily and Hourly CVaR Assessment

Cross Entropy Covariance Matrix Adaptation Evolution Strategy Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets with Renewables

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