Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty

Shen, Hongtao; Tao, Peng; Lyu, Ruiqi; Ren, Peng; Ge, Xinxin; Wang, Fei

doi:10.1016/j.egyr.2021.07.021

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…Load aggregator (LA) can realize the coordination and optimization of various load side resources, it can control a large amount of distributed energy effectively, fully tap the response capacity of distributed energy, and facilitate the centralized scheduling and control of the system [4]. Reference [5] shows a photovoltaic system with a battery energy storage unit and a LA-integrated residential customers that balance the power by optimizing dispatch and bidding in Day-ahead and real-time markets based on real-time electricity price. Reference [5] shows a new distributed coordination decision-making strategy for LA base on load agent, which considers the uncertainty of response and solves the uncertainty of risk aggregator participating in demand response, indicating that the participation of load aggregators is conducive to the decentralized coordination between the upper and lower layers of decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome the limitations and shortcomings of the above two methods, the distributionally robust optimization (DRO) which combines the conditional value at risk (CVaR) tools commonly used in the economic portfolio field, has become a research hotspot in recent years [16]. Reference [5] shows a day-ahead multi-objective UC model with CVaR and ultra-low emission problem, which used ambiguity set theory to deal with the uncertainty of wind power and future load. Reference [18] shows an ambiguity set of the uncertainties of wind power and photovoltaic output forecasting errors based on a Wasserstein metric and, in conjunction with chance constraints, limited the expected probability of the inequality constraint with uncertain variables to the lowest allowable confidence level, a data-driven distributionally robust chance constraints optimization model is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [5] shows a photovoltaic system with a battery energy storage unit and a LA-integrated residential customers that balance the power by optimizing dispatch and bidding in Day-ahead and real-time markets based on real-time electricity price. Reference [5] shows a new distributed coordination decision-making strategy for LA base on load agent, which considers the uncertainty of response and solves the uncertainty of risk aggregator participating in demand response, indicating that the participation of load aggregators is conducive to the decentralized coordination between the upper and lower layers of decision-making. In the above market clearing model, the aggregator acts as an intermediary between the system operator and distributed energy [7], due to the lack of consideration of interaction among system operators, power plants and load aggregator, this paper establishes a multi-participant framework for day-ahead electricity market.…”

Section: Introductionmentioning

confidence: 99%

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A Distributionally Robust Bilevel Optimal for Low-Carbon Power System Considering Load Uncertainty

Zhang,

Lai,

Liang

2023

Preprint

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With the depletion of fossil energy worldwide, new energy power generation represented by photovoltaic, wind power, etc. is being applied, which introduces a nonnegligible supply and demand balance difficulty to the power grid. Relying solely on the regulating capacity of traditional power plants is insufficient, it is urgent to awake demand-side resources and construct a multi objective optimization model that considers the interests of both supply and demand to achieve flexible interaction between power users. To address this problem, a multi objective distributionally robust bilevel optimization model based on KL divergence is proposed, which optimizes system operation cost, carbon emission and unit profit, while resolving the self-scheduling problem under load and price uncertainties. Additionally, the GARCH model is introduced and applied to the Value at Risk theory, which characterizes the fluctuation aggregation and heteroscedasticity of the daily load change of the load aggregator, so that people can grasp the risk more accurately. Subsequently, a novel approach for bilevel programs based on Wolfe duality is employed to transform the bilevel optimization problem into a single level optimization problem, and the nonlinear multi objective problem is directly solved by GUROBI. Finally, the simulation of the case shows that the equivalent DR model can realize efficient allocation and complementary optimization of flexible resources in the market environment and improve the operation economy and stability of the power system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Distributionally Robust Bilevel Optimal for Low-Carbon Power System Considering Load Uncertainty

Zhang,

Lai,

Liang

2023

Preprint

View full text Add to dashboard Cite

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“…An optimal coordinated bidding strategy for the wind, solar, and pumped storage cooperative (WSPC) is implemented to facilitate the revenue distribution among participating members of the large-scale day-ahead power market [19]. Shen et al [20] discussed the optimal scheduling and bidding strategy for residential customers having PV systems integrated with battery energy storage (BES) and taking part in day-ahead (DA) and real-time (RT) markets to maximize the profits for load aggregators. Considering uncertainty in wind power and electricity prices, a three-stage stochastic optimization problem is formulated for the joint operation of a compressed air energy storage (CAES) aggregator and a wind power aggregator participating in the day ahead, intra-day, and balancing markets [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

et al. 2022

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PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier's bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms. INDEX TERMSWind farm, locational marginal price, Monte-Carlo simulation, artificial gorilla troops optimizer algorithm, slime mould optimizer algorithms.

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“…Moreover, regulating the output voltage cannot be carried out directly without involving the inductor current (Bouchekara et al, 2021). Thus, the system requires a sensor to continuously measure the current (Shen et al, 2021); however, installing the sensor can make the system quite vulnerable to disturbance and raise construction costs (Abdelmalek et al, 2020). Therefore, to reach a suitable control performance for the system, a highperformance control is needed to have good disturbance rejection, less steady-state error, small overshoot, a fast recovery time, and fast transient response time (Mehdi et al, 2020;Mobayen and Tchier, 2018).…”

Section: Introductionmentioning

confidence: 99%

Control of Boost Converter Using Observer-Based Backstepping Sliding Mode Control for DC Microgrid

et al. 2022

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The output voltage of a photovoltaic (PV) system relies on temperature and solar irradiance; therefore, the PV system and a load cannot be connected directly. To control the output voltage, a DC-DC boost converter is required. However, regulating this converter is a very complicated problem due to its non-linear time-variant and non-minimum phase circuit. Furthermore, the problem becomes more challenging due to uncertainty about the output voltage of the PV system and variation in the load, which is a non-linear disturbance. In this study, an observer-based backstepping sliding mode control (OBSMC) is proposed to regulate the output voltage of a DC-DC boost converter. The input voltage of the converter can be a DC energy source such as PV-based microgrid systems. An adaptive scheme and sliding mode controller constructed from a dynamic model of the converter is used to design an observer. This observer estimates unmeasured system states such as inductor current, capacitor voltage, uncertainty output voltages of the PV cell, and variation of loads such that the system does not need any sensors. In addition, the backstepping technique has been combined with the SMC to make the controller more stable and robust. In addition, the Lyapunov direct method is employed to ensure the stability of the proposed method. By employing the proposed configuration, the control performance was improved. To verify the effectiveness of the proposed controller, a numerical simulation was conducted. The simulation results show that the proposed method is always able to accurately follow the desired voltage with more robustness, fewer steady-state errors, smaller overshoot, faster recovery time, and faster transient response time. In addition, the proposed method consistently produces the least value of integral absolute error.

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Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty

Cited by 16 publications

References 39 publications

A Distributionally Robust Bilevel Optimal for Low-Carbon Power System Considering Load Uncertainty

A Distributionally Robust Bilevel Optimal for Low-Carbon Power System Considering Load Uncertainty

Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

Control of Boost Converter Using Observer-Based Backstepping Sliding Mode Control for DC Microgrid

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