Dispatch Of Virtual Power Plant Research Articles

Low-Carbon Economic Dispatch of Virtual Power Plant Considering Hydrogen Energy Storage and Tiered Carbon Trading in Multiple Scenarios

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Economic scheduling of virtual power plant in day-ahead and real-time markets considering uncertainties in electrical parameters

The virtual power plant (VPP) concept, which offers energy efficiency, benefits significantly from the tremendous potential of integrating distributed energy resources (DERs). This work proposes optimal economic dispatch of virtual power plant (VPP), and the problem is formulated in a novel way considering the potential of energy storage systems (ESS) of electric vehicles (EVs) and data center (DC) in a data center facility test model using optimized energy management system (EMS). The problem is evaluated for maximizing the revenue for VPP considering market prices and the uncertainties in the distributed energy resources. To this purpose, a techno-economic analysis of the proposed test system of VPP is evaluated using mixed-integer linear programming (MILP). Renewable generation, electricity price, load demand, and real-time EV behavior uncertainty analysis are forecasted using Long Short-Term Memory (LSTM) along with DC battery degradation analysis which constitutes the optimization model. The results showed that the VPP EMS can perform optimal power scheduling with the day ahead and real-time electricity price scenarios. Moreover, further analysis including battery degradation cost of DC UPS ESS proved the capability of the proposed framework by up to 2064.4 $/h revenue for day-ahead price and 562 $/h revenue for the real-time price compared to revenue without battery degradation cost. A simulation scenario is also carried out where the constant load demand and the battery storage powers are varied between 50% and 150%. It can be concluded that the 500 kW and 750 kW ESS (100% and 150%) are better choices as compared to 250 kW (50%) during increased load demand for investors due to their higher revenue. Hence it is concluded that this research highlights the importance of revenue benefits from deploying VPP with the collaborative operation of DERs.

Low-carbon optimal dispatch of virtual power plant based on time-of-use ladder carbon emission rights exchange mechanism

In view of the problems of abandoning wind and solar energy, and carbon emissions, inevitable reduce of thermal power generation caused by the consumption of wind and solar energy, and the changes in the proportion of wind and solar energy consumption to thermal power at different times under the influence of time-of-use price, a time-of-use ladder carbon emission rights exchange mechanism is proposed, and on this basis, a low-carbon virtual power plant optimal dispatching model with ‘wind-solar-gas-storage’ is built. The maximum daily operating income of the virtual power plant is taken as the objective function in the model. The IBM commercial solver CPLEX is employed to optimize the solution, so as to obtain the optimal economic dispatching strategy of the virtual power plant, and compares it with the set scenarios. The simulation results reveal that the proposed mechanism can effectively enhance the enthusiasm and operating benefits of virtual power plants for renewable energy consumption, and reduce carbon emissions, providing new methods for low-carbon dispatching, and wind and solar energy consumption of virtual power plants.

Real‐time active power dispatch of virtual power plant based on distributed model predictive control

With the increasing penetration of renewable energy, virtual power plants (VPP) reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in VPP has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control (DMPC), so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbour information. Simulation results show the feasibility and effectiveness of the proposed method.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources, which have become a new operation mode with substantial advantages such as high flexibility, adaptability, and economy. This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources. The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments. In this regard, the faults of stochastic optimization and traditional robust optimization can be overcome. Firstly, a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed, and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets. Then, an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem, which improves the computational efficiency of the model. Finally, the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy. They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

Receding‐horizon based optimal dispatch of virtual power plant considering stochastic dynamic of photovoltaic generation

AbstractThe merits of a virtual power plant in integrating photovoltaic generation and flexible loads, such as a chilled water thermal storage air conditioning system and an electric vehicle, are well recognized. However, the optimal operation of a virtual power plant is challenged by the complexities of solar irradiance and the large size of a chilled water thermal storage air conditioning system and an electric vehicle. This paper proposes a new approach to the optimal dispatch problem of a virtual power plant. The stochastic dynamic of solar irradiance is modelled by a stochastic differential equation set. The binary decision for a chilled water thermal storage air conditioning system and an electric vehicle are characterized by a mixed logical dynamical model. The resulting optimal dispatch problem is solved by the receding horizon approach. The appeal of the proposed approach is in its capability to consider the stochastically dynamical impact of solar irradiance. Besides, the proposed approach can solve the optimization problem over a relatively small period of time, and thus has the potential for online applications. Finally, the feasibility and effectiveness of the proposed approach is demonstrated by numerical simulations.

Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

Bi-Level Real-Time Economic Dispatch of VPP Considering Uncertainty

In this paper, we propose a bi-level, real-time economic dispatch method of a virtual power plant (VPP), including various distributed energy resources (DERs). Considering the different interests of VPPs and a system operator, the real-time economic dispatch of VPPs is described as a bi-level programming problem, where a system operator dispatches VPPs based on the price incentive mechanism on the upper level, and the VPPs provide response according to the optimal control of their DERs on the lower level. Considering the uncertainties of DERs and loads, the decision risks of a system operator on the upper level and VPPs on the lower level are further dealt with by the fuzzy chance constrained programming, such that they can make reasonable decisions according to their own preferred risks. The mapping method and the bi-level optimization method are also presented as the solutions for the proposed model. In this way, the fuzzy chance constraints and objective functions of both levels are transformed into deterministic forms and, then, are calculated dispersedly. As a result, the calculation burden of a system operator and the information privacies of VPPs all can be treated availably. The case studies verify the effectiveness of the proposed method in the end.

Optimal dispatch of virtual power plant using interval and deterministic combined optimization

Virtual power plant (VPP) technology is a promising solution to manage the uncertainties of renewable energy in demand side. Because of various uncertainties, VPPs’ dispatch models are always solved by stochastic optimization, robust optimization and interval optimization. However, these approaches always require high computational complexity, be over conservative or cannot describe VPPs’ profitability precisely. Thus, this paper combined interval and deterministic optimization together and adopted the combined approach to solve a VPP’s dispatch problem. The combined optimization not only maximized VPPs’ deterministic profits under forecasted scenarios to estimate the VPP’s most likely profits, but also maximized VPPs’ profit intervals to manage uncertainties. The proposed model was in a regulated electricity market environment, and the VPP’s traded energy was cleared by time-of-use prices. A case study from real world was adopted to prove the validity of this model. Comparison with other optimizations like stochastic and robust optimization was also studied. The combined optimization can manage the VPP’s uncertainties within limited computational time.

A fully distributed ADMM-based dispatch approach for virtual power plant problems

In this paper, a fully distributed approach is proposed for a class of virtual power plant (VPP) problems. By characterizing two specific VPP problems, we first give a comprehensive VPP formulation that maximizes the economic benefit subjected to the power balance constraint, line transmission limits and local constraints of all distributed energy resources (DERs). Then, utilizing the alternating direction method of multipliers and consensus optimization, a distributed VPP dispatch algorithm is developed for the general VPP problem. In particular, Theorem 1 is derived to show the convergence of the algorithm. The proposed algorithm is completely distributed without requiring a centralized controller, and each DER is regarded as an agent by implementing local computation and only communicates information with its neighbors to cooperatively find the globally optimal solution. The algorithm brings some advantages, such as the privacy protection and more scalability than centralized control methods. Furthermore, a new variant of the algorithm is presented for improving the convergence rate. Finally, several case studies are used to illustrate the efficiency and effectiveness of the proposed algorithms.

Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network

A virtual power plant (VPP) is aimed to integrate distributed energy resources (DERs). To solve the VPP economic dispatch (VPED) problem, the power supply-demand balance, power transmission constraints, and power output constraints of each DER must be considered. Meanwhile, the impacts of communication time delays, channel noises, and the time-varying topology on the communication networks cannot be ignored. In this paper, a VPED model is established and a distributed primal-dual sub-gradient method (DPDSM) is employed to address the presented VPED model. Compared with the traditional centralized dispatch, the distributed dispatch has the advantages of lower communication costs and stronger system robustness, etc. Simulations are realized in the modified IEEE-34 and IEEE-123 bus test VPP systems and the results indicate that the VPED strategy via DPDSM has the superiority of better convergence, more economic profits, and stronger system stability.

Risk assessment and bi‐level optimization dispatch of virtual power plants considering renewable energy uncertainty

Aiming at the effective management of distributed energy resources (DERs), we propose a bi‐level optimization dispatch model which consists of a virtual power plant (VPP) and an independent system operator (ISO) in order to achieve a coordinated dispatch between the upper level objective function of minimizing system's operation cost and the lower level objective function of maximizing the VPP's net profit. However, the VPP's dispatch strategy is variable due to the threat from unavoidable uncertainties including unit outage, load forecast deviation, and renewable energy forecasting error. In order to evaluate the influences of these risks on VPP's dispatch strategy, a proper definition of expected energy not supplied (EENS) and its relationship with VPP's reserve capacity are provided. Also, chance‐constrained programming (CCP) is adopted during the analysis. By including VPP's risk cost into the dispatch model, VPP's dispatch strategy is able to keep a balance between the potential risk and economic profit. In addition, a further discussion regarding the risk cost is conducted in view of renewable energy capacity proportion and the VPP capacity proportion. Based on the analysis results of the case study provided, the proposed model serves as a foundation for VPP's economic and risk‐balanced dispatch.

Economic Optimal Dispatch of Virtual Power Plant Based on Time-of-Use Power Price

Economic Optimal Dispatch of Virtual Power Plant Based on Time-of-Use Power Price

Optimal scheduling of virtual power plant with battery degradation cost

This study proposes a novel optimal generation scheduling model for virtual power plant (VPP) considering the degradation cost of energy storage system (ESS). The VPP is generally formed by a mix of distributed energy resources, and the ESS is an important installation for flexible VPP dispatch due to its controllable and schedulable behaviours. For the operations of battery storage systems, the ambient temperature and depth of discharge have significant impacts on the wear and tear of the ESS as well as battery degradation cost. Furthermore, the battery degradation cost is modelled and approximated by a piecewise linear function, and then incorporated into the proposed optimal VPP scheduling model. Consequently, the optimal VPP scheduling problem is formulated as a two-stage stochastic mixed-integer linear programming in order to maximise the expected profits of the VPP. The proposed model has been successfully implemented and tested through a representative case study, and the influence of battery degradation cost on optimal VPP scheduling has also been thoroughly analysed and demonstrated.

Stochastic Optimal Dispatch of Virtual Power Plant considering Correlation of Distributed Generations

Virtual power plant (VPP) is an aggregation of multiple distributed generations, energy storage, and controllable loads. Affected by natural conditions, the uncontrollable distributed generations within VPP, such as wind and photovoltaic generations, are extremely random and relative. Considering the randomness and its correlation of uncontrollable distributed generations, this paper constructs the chance constraints stochastic optimal dispatch of VPP including stochastic variables and its random correlation. The probability distributions of independent wind and photovoltaic generations are described by empirical distribution functions, and their joint probability density model is established by Frank-copula function. And then, sample average approximation (SAA) is applied to convert the chance constrained stochastic optimization model into a deterministic optimization model. Simulation cases are calculated based on the AIMMS. Simulation results of this paper mathematic model are compared with the results of deterministic optimization model without stochastic variables and stochastic optimization considering stochastic variables but not random correlation. Furthermore, this paper analyzes how SAA sampling frequency and the confidence level influence the results of stochastic optimization. The numerical example results show the effectiveness of the stochastic optimal dispatch of VPP considering the randomness and its correlations of distributed generations.

Summary on Optimal Dispatch of Virtual Power Plant

虚拟电厂将多个分布式电源、储能装置和负荷等分布式单元通过先进的通信技术和控制策略有效集成为可控的独立发电厂，以规模化的手段有效解决分布式电源并网的控制调度问题。本文分别从信息交互架构、运行管理架构、辅助服务、优化调度等方面对虚拟电厂进行了全面综述并深入探究了虚拟电厂能量管理优化调度问题。对虚拟电厂优化调度问题主要从优化模型中的优化目标、调度周期以及虚拟电厂内不确定性因素的处理3个方面进行了详细分析。文章最后还指出对虚拟电厂组织构成、调度管理、运行控制等各方面的研究仍待深入探索，形成可实用化的理论框架。 Virtual Power Plant integrated large numbers of distributed generations, energy storage devices and loads into a controllable independent power plant by advanced communication technolo- gies and control strategies. It can solve the control and dispatch problems of distributed generations connecting to network effectively by a series of large-scale means. In this paper, we made a comprehensive review on virtual power plant from the aspects of information exchange architectures, operation and management structures, ancillary services and optimal scheduling respectively as well as explored the energy management optimization scheduling problems of virtual power plant deeply. For the optimal scheduling problems, we analyzed in detail mainly from three aspects of optimization goals, scheduling periods and the uncertainties in virtual power plant. Finally, some suggestions are given for further studies of virtual power plant on organizational structure, scheduling management, operational control and so on, so as to form a practical theoretical framework.

Distributed optimal dispatch of virtual power plant based on ELM transformation

To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dispatch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.

Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication

In this letter, a distributed optimal dispatch method based on the distributed primal-dual sub-gradient algorithm is proposed. By coordinating individual decision-making of distributed energy resources (DERs) in the virtual power plant (VPP) via limited communication, the profit of VPP can be maximized. It can be proven that the proposed distributed algorithm has similar performance to the centralized dispatch.