•The modeling of stochastic scheduling problem in a virtual power plant.•Scheduling the resources considering the uncertainty of thermal and electrical loads.•An accurate model for uncertain wind ...power generation.•The effects of PV thermal units in the scheduling problem.
For coordinated operation of distributed energy resources, virtual power plants are introduced as a solution to maximize the net profit of all participants considering the uncertainties. In this study, a stochastic scheduling problem for a virtual power plant is modeled to meet the thermal and electrical load considering the network security constraints and uncertainties of electrical and thermal loads, wind speed, solar radiation, and market price. The virtual power plant consists of conventional generators, photovoltaic panels, wind turbines, photovoltaic-thermal panels, combined heat and power, energy storage systems, and boilers. To model all uncertain parameters, a scenario reduction approach is used to decrease the number of possible scenarios. Also, to increase the accuracy of the uncertainty modeling, a new approach for modeling the wind speed uncertainty is proposed. By utilizing a proper linear model for the conventional generators, the scheduling problem is formulated as a mixed integer linear programming. Two cases are introduced to study the effects of including photovoltaic-thermal panels in the scheduling problem using the IEEE 33-bus distribution test system. The problem is modeled in GAMS environment and is solved using CLPEX solver. The results show that the proposed model for virtual power plants scheduling for the next 24 h increases the expected net profit. Simulation results show that considering photovoltaic-thermal panels increases the expected net profit. Also, addition of photovoltaic-thermal panels has decreased the dependency of the virtual power plant on the boiler and combined heat and power for meeting the thermal load. A sensitivity analysis is performed to investigate the effects of electricity price on virtual power plant profit.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, brings a significantly disruptive and transformational ...impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users’ independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users’ energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users’ cost by up to 38.6% and reduces the overall system cost by 11.2%.
•A blockchain-based energy management platform is developed for a VPP.•Energy trading among users and network services provided to the grid are modeled.•A distributed optimization algorithm is designed to manage the VPP system.•A blockchain system is implemented for the developed energy management platform.•Simulations using real-world data demonstrate the benefits to users in the VPP.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A stochastic framework is presented for short-term energy and reserve scheduling.•Uncertainties and N-1 contingencies are presented in VPP’s scheduling problem.•VPP’s offering/bidding strategies are ...investigated through incorporation of CVaR.•The economic benefit of reserve services by demand response actions is investigated.
In this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP’s profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the VPP energy and reserve management and its security indices are investigated.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This article proposes an optimal coordinated scheduling of electric vehicles (EVs) for a virtual power plant (VPP) considering communication reliability. Recent advancements on wireless technologies ...offer flexible communication solutions with wide coverage and low-cost deployment for smart grid. Nevertheless, the imperfect communication may deteriorate the monitoring and controlling performance of distributed energy resources. An interactive approach is presented for combined optimization of dynamic spectrum allocation and EV scheduling in the VPP to coordinate charging/discharging strategies of massive and dispersed EVs. In the proposed approach, a dynamic partitioning model of the multi-user multi-channel cognitive radio is used to cope with the vehicle-to-grid (V2G) communication issue due to variable EV parking behaviors, and a two-stage V2G dispatch scheme is proposed for the wind-solar-EV VPP to maximize its overall daily profit. Furthermore, the effects of packet loss probability on the VPP scheduling performance and battery degradation cost are thoroughly analyzed and investigated. Comparative studies have been implemented to demonstrate the superior performance of the proposed methodology under various imperfect communication conditions.
•Three major uncertainties of VPP are classified and reviewed comprehensively.•Detailed mathematical descriptions are summarized to quantize the uncertainties.•Components of objective functions and ...constraints with uncertainties are refined.•Tools and platforms for the optimization of VPP with uncertainties are presented.•Three demonstration projects of VPP considering uncertainties are introduced.
A virtual power plant (VPP) is a system that integrates several types of power sources, so as to give a reliable and friendly overall power supply. The sources are often a cluster of distributed generation systems with intermittent renewable energies. Uncertainties are the important issues in researches and applications of VPP. In this paper, renewable power, market price and load demand are classified as major factors of uncertainties, and a comprehensive review of these three factors are given. Based on the classification, the detailed mathematical descriptions are summarized. And then, optimization objectives and constraints, which are adopted to improve the running performance of VPP with uncertainties, are summed up systematically. Solution approaches and tools for the optimization are also presented. At last, demonstration projects are introduced to show how uncertainties are handled in practice. This review paper can provide a rational assistance for researchers who focus on VPP.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Design a novel structure of virtual power plant connected with gas-power plant carbon capture, power-to-gas and waste incineration power (GPW-VPP).•Propose a nearly-zero carbon optimal operation ...model for GPW-VPP based on the information gap decision theory and fuzzy satisfaction theory.•Construct a Nash negotiation-based benefit allocation strategy for GPW-VPP considering the contribution factors of risks, benefits and carbon emissions.•Select the Lankao Rural Energy Revolution Pilot as the simulation system for verifying the effectiveness and applicability of the proposed.•Discuss the optimal operation strategy of the GPW-VPP under the extremely worst scenario.
Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaics, and decentralized wind power, this study designed a novel structure of a virtual power plant connected with gas-power plant carbon capture (GPPCC), power-to-gas (P2G), and waste incineration power (WI), namely, a GPW-VPP. Then, the information gap decision theory (IGDT) and fuzzy satisfaction method were applied to construct a nearly-zero carbon optimal operation model. In this model, the maximum revenue and minimum carbon emissions were selected as the initial objectives, which were converted into one maximum satisfaction objective. Three uncertainty variables, namely, wind power, photovoltaic power, and user’s load, were described using the IGDT. Secondly, to optimize the distribution of the cooperative operation revenue for the entities in GPW-VPP, a Nash negotiation-based benefit allocation strategy is established considering the multidimensional contribution factors of risks, benefits, and carbon emissions. Finally, the Lankao Rural Energy Revolution Pilot program in China was selected as the case study, the results showed: (1) GPW-VPP can aggregate and utilize different types of distributed energy sources such as rural wind power plants (WPPs) and photovoltaic power generation (PVs) to realize the electricity–carbon–electricity cycle effect. (2) The proposed operation optimization model can measure the uncertainty risk and formulate an optimal plan considering the above dual objectives. When the deviation coefficient of the predicted objectives is 0.5, the uncertainty degree is 0.142, and the cost of the decision plan is less than the expected cost of decision maker. Compared with the maximum revenue objective, the operation revenue and carbon emissions reduced by 4.6% and 35.76% under the comprehensive optimization objective, respectively, (3) The proposed benefit distribution strategy can be used to formulate a better benefit distribution plan that meets the comprehensive contributions of multiple entities. Affected by the risk of output uncertainty, the benefit proportion of WPP and PV increased, but it was 1.64% lower than that in the traditional distribution plan. Affected by carbon emissions, the benefit proportion of biomass power generation decreased, but it was 0.57% higher than that in the traditional distribution plan. Overall, the proposed operation optimization model and benefit distribution strategy can balance the interest requirements of different entities and promote the optimal aggregation and utilization of rural distributed energy resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper presents a methodology for estimating the optimal amount of automatic frequency restoration reserve provided by an aggregation of renewable power plants. The increasing penetration of ...distributed weather-dependent renewable generation presents a challenge to grid operators. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently hinders their integration into reserve mechanisms. In the methodology developed a quantile regression forest model is used to forecast the aggregated production and a copula-based approach integrates the dependence between prices and renewable production. We then propose and compare three strategies to derive an optimal quantile of the combined production forecasts that can be used as basis to provide a reliable ancillary service to the System Operator. The methodology is evaluated using historical prices for energy and automatic frequency restoration reserve along with production measurements from the several renewable power plants.
Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be ...solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Virtual power plants (VPPs) have become a driving force for the decentralized energy industry, due to their efficient management and control of distributed energy resources. Most of the operation ...strategies for VPPs are designed based on the day-ahead forecasts. However, the prediction errors of the renewable energy sources (RES) and loads in the power dispatch schedule can lead to a suboptimal operation. In this article, an adaptive and predictive energy management strategy for a real-time optimal operation of VPPs is proposed based on the model predictive control technique with a feedback correction (FC) to compensate for the prediction error. This strategy has two parts: 1) receding-horizon optimization (RHO), and 2) FC. In the first part, a hybrid prediction algorithm based on the integration of the time-series model and the Kalman filter is used to forecast the output powers of RES and the loads. Based on the prediction, the RHO model schedules the operation following the latest forecast information. In the second part, the receding schedule is adjusted based on the fast-rolling gray model's ultrashort-term error prediction. The FC is applied to minimize the adjustments for compensating the prediction error. The proposed strategy is implemented on a VPP in a real electricity distribution system in New South Wales, Australia. The simulation results demonstrate the effectiveness of the proposed strategy with a better tracking of the actual available resources and a minimal mismatch between demand and supply.
PDF
