Data-Driven Resource Planning for Virtual Power Plant Integrating Demand Response Customer Selection and Storage

Liang, Huishi; Jin, Ma

doi:10.1109/tii.2021.3068402

Cited by 38 publications

(13 citation statements)

References 27 publications

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“…Reference [20] has not considered the effect of limiting the number of hours allowed to a demandside management program. Reference [9] has considered the number of hours allowed for the demand response program, but the number of times the battery is charged and discharged has not been presented, as well as grid modeling. Also, the effect of limiting the number of times the demand response program is allowed is not shown in the results.…”

Section: A Motivation and Contributionmentioning

confidence: 99%

“…The equation (8) shows shiftable demands at the time of t. The constraint (9) represents the limits of the substation demand changes at the time of t in the demand response program. Equation (10) assures which is the sum of shifting demands obtained from the demand response program is equal to the sum of the initial demands.…”

Section: Cde(45i)mentioning

confidence: 99%

“…In [8] the authors presented an improved gray wolf optimization algorithm to solve the optimal scheduling problem for battery energy storage systems (BESS), taking into account the mass integration of renewable energy resources, such as solar and wind energies in active distribution networks. Reference [9] proposes a data-based approach to virtual power plant resource planning, in which the amount of battery energy storage and customer choice of demand response are synergistically optimized to maximize the profitability of virtual power plants in the electricity market. Reference [10] examines the effect of energy storage systems' performance on voltage stability and quality of local power systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

et al. 2023

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The use of energy storage power plants (ESPP) seems necessary to create flexibility in the operation of smart grids and increase economic benefits. The power storage power plants connect directly to the smart distribution substation (DS), saving energy costs and increasing energy efficiency. Also, demand response (DR) program management has improved the performance of smart grids by shifting loads. In this regard, the main challenge is the optimal coordination between these two problems by considering control challenges such as limiting the number of charge/discharge times and the number of hours of DR implementation using a linear model that can be solved with powerful commercial solvers. In this paper, we proposed an economic mixed-integer linear programming (MILP) model for optimal coordination of DR and large-scale ESPP considering the practical limitations of charge/discharge times and DR action times with optimal management of distributed generation (DG) resources, which provides more realistic results due to the constraints in operating times in both DR and ESPP. To validate and analyze the proposed model, a standard 33-bus distribution network with a standard Vanadium redox battery power plant and a large 874-bus system with three Vanadium redox battery power plants are considered. Various scenarios have been considered to demonstrate the constraints imposed on the demand side management program and battery charge and discharge, the results of which show that these constraints have a significant impact on the objective function of the problem. Also, by comparing the proposed method with other methods, it was found that the proposed method is more efficient in improving the objective function and limiting options. INDEX TERMS Energy Storage power plant, Distribution Substation, Demand response program, Mathematica model.

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Section: A Motivation and Contributionmentioning

confidence: 99%

Section: Cde(45i)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

et al. 2023

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“…At present, China's power grid has retained nearly 500 million customer data, which come from data collected by power grid energy meters, customer telephone service system data, power grid management data, etc. [1]. Based on these data, using big data analysis technology to analyze power customers, we can build a multi-level and multi-dimensional power customer's portrait [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Extraction Method of Power Customer’s Portrait based on Knowledge Map and Label Extraction

Li¹,

Ji²

2022

IJACSA

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In order to realize the visualization of power customer characteristics and better provide power services for power customers, a multi-feature extraction method of power customer's portrait based on knowledge map and label extraction is studied. The power customer's portrait construction model is designed, which uses the knowledge map construction link to collect the power customer related data from the power system official website and database, and clean and convert the data; In the multi-feature analysis section, natural language processing technology is used to analyze the characteristics of power customers through Chinese word segmentation, vocabulary weight determination and emotion calculation; Based on the feature analysis results, the portrait label is extracted to generate the power customer's portrait. The power customer's portrait is used to realize the application of power customer's feature visualization, power customer recommendation, power customer evaluation and so on. The experimental results show that this method can effectively construct the knowledge map of power customers, accurately extract the characteristics of power customers, generate labels, and realize the visualization of power customer's portraits.

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“…Although a VPP enables control and optimization of energy generation, unfortunately when it has a high penetration of RGs, it may need an energy storage system (ESS) to reduce RGs' uncertainties [3], [4], which results in high investment costs. In fact, without ESS, VPP's decisionmaking confronts risk due to forecasting error of RGs' output in real-time operation.…”

mentioning

confidence: 99%

Investigating the Impact of External Demand Response Flexibility on the Market Power of Strategic Virtual Power Plant

et al. 2022

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In this paper, a decision-making framework is proposed for a virtual power plant (VPP) to participate in day-ahead (DA) and regulating market (RM) considering internal demand response (IDR) flexibility. In the proposed model, a DR exchange market (DRXM) is also introduced to cover deviations of uncertain resources and decrease VPP's imbalance penalties in the RM. The VPP can optimize its procurement expenditures by providing DR services from both IDR providers and DRXM. A market inefficiency index (MII) is defined to analyze the effect of trading energy in the DRXM on the market power of the VPP. The proposed model is formulated as a bi-level problem, in which at the upper level, the VPP maximizes its profit while at the lower level, the distribution system operator (DSO) strives to clear both DA and RM markets to maximize social welfare. The proposed problem is nonlinear and converted into a linear single-level problem through Karush-Kuhn-Tucker (KKT) optimality conditions and duality theory. The simulation results show that in high external demand response (EDR) participants, the expected profit of the VPP augments about 3% which is a substantial value for the one-day scheduling horizon. Furthermore, by providing EDR services, MII reduces which implies the EDRs preserve their economic surplus.

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Data-Driven Resource Planning for Virtual Power Plant Integrating Demand Response Customer Selection and Storage

Cited by 38 publications

References 27 publications

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

Multi-Feature Extraction Method of Power Customer’s Portrait based on Knowledge Map and Label Extraction

Investigating the Impact of External Demand Response Flexibility on the Market Power of Strategic Virtual Power Plant

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