분산 재생에너지의 효율적 활용을 위한 가상발전소(VPP) 플랫폼 개발에 관한 연구

With the global demand to increase the level of low-carbon renewable energy resources (RERs) for electric power generation, dwindling fossil fuel reserves, and concerns that fossil fuel emissions are leading to climate change with possibly disastrous consequences, research is underway to discover the probable large-scale usage of RERs that will not be integrated into an existing power grid. A large-scale RER power grid is anticipated to run independently and be stable and dependable, like a traditional system. Wind power generation is no longer negligible. Wind energy is one of the most established renewable power options for ensuring RE self-sufficiency, but it's also one of the ficklest (RERs). Wind power generation is predicted to rise quickly in the next years owing to interest in renewable energy to counteract global warming. National or regional RE power networks are the subjects of growing study. The issue is the grid's stability and dependability. Wind farms' production variability, intermittency, and load mismatch can damage grid voltage stability. In answer to this difficulty, a large-scale wind power system was modeled using a stochastic methodology, and the results were analyzed using the Lyapunov technique, matrix exponential, and Hurwitz criteria to determine the future stability of a 100% RE grid.

In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of the power peaks is a new objective and open issue to a well-functional EMS, along with the maximization of profit in the energy market. Thus, both objectives have to be taken into account at the same time. Thus, this paper proposes the EMS application incorporating power offering strategy applying a nature-inspired algorithm such as Particle Swarm Optimization (PSO) algorithm, in order to find the optimal solution of the objective function in the context of the overall operating cost, the coordination of DERs, and the energy losses in a MG integrated VPP network. For a fair DERs coordination with minimized power fluctuations in the power flow, the power offering strategies with an active power control and re-distribution are proposed. Simulation results show that the proposed MG integrated VPP model with PSO-based EMS employing Egalitarian reDistribution (ED) power offering strategy is most feasible option for the overall operating cost of VPP revenue. The total operating cost of the proposed EMS with ED strategy is 40.98$ compared to 432.8$ of MGs only without EMS. It is concluded that each MGs in the proposed VPP model intelligently participates in energy trading market compliant with the objective function, to minimize the overall cost and the power fluctuation.

In the current European electricity market, if the actual generation is less than the scheduled contract or excess generation is injected into the grid, a penalty for deviation may be applied to market participants. Virtual power plants (VPPs) are trying to improve their forecasting technologies for managing this risk and increasing their expected profit. However, in this study, the authors would choose another approach to reduce the risk. To minimise the variance of a sum of outputs of all distributed renewable energy resources (DRESs), the covariance between the output of each DRES should be minimised. This covariance depends on the correlation between the outputs of DRESs. If the DRESs in the same area are aggregated as a single VPP unit, the correlation would be high and the variance of the VPP will be large, accordingly. On the other hand, if the DRESs in separated regions are collected as one VPP unit, the correlation between DRESs and the variance of the VPP may be smaller than in the former case. Using this feature, in this study, the authors would find the optimal combinations of VPP compositions with a minimum weighted average of variance of m VPP units among N DRESs based on historical output data.

16 - Emission impacts on virtual power plant scheduling programs

The operating income of power supply virtual power plants is greatly affected by factors such as weather, external electricity prices and popular policy mechanisms. How to sort out and analyze the factors affecting the participation of power supply virtual power plants in the market operation and how to enhance the flexibility of virtual power plant operation decision becomes an urgent problem to be solved. This paper proposes a system dynamics-based approach to analyze the factors influencing the market operation of power supply virtual power plants. First, a system dynamics model of power supply virtual power plants participating in the day-ahead market is built, dividing the distributed generation (DG) module and the day-ahead market module. Then analyze the impact of weather and real-time electricity prices on the short-term operation of virtual power plants. On the basis of this, a system dynamics model of the long-term market for power supply virtual power plants is built, taking into account the impact of the green certificate trading mechanism and carbon trading mechanism on the long-term operation of virtual power plants. By simulating the impact of different factors and policy mechanisms on the strategies of power supply virtual power plants participating in the day-ahead market and long-term market through arithmetic examples, we provide a theoretical basis for the adjustment of bidding strategies of power supply virtual power plants participating in the market and constructive suggestions for the promotion of virtual power plants and different policy mechanisms.

Virtual power plant (VPP) is an integrated class of integrated power plant composed of energy management system and small and micro distributed energy resources controlled by it. Its distributed energy sources can be distributed generator sets, distributed energy storage equipment or demand response resources distributed among many demand-side users. Its concept emphasizes more on the function and effect presented to the outside world, updating the operation concept and produce social and economic benefits, in line with the needs and direction of China's power development. Given the uncertainty in renewable energy output and demand response, the VPP market of gas networks, Power to Gas (P2G) equipment, flexible loads and other resources are introduced. Furthermore, VPP's optimal scheduling strategy includes considering power and gas scheduling for both power and gas flows, and studies the optimization model considering the dual energy market to maximize the profits of VPP. Through example studies, the economics and scheduling strategies of the proposed VPP are analyzed. Simulation results show that the VPP proposed in this paper can effectively reduce the proportion of renewable energy emissions reduction by selling power or P2G equipment. Moreover, it is conducive to maintaining the pressure stability and safe operation of the natural gas power grid. VPP can reduce the cost of unbalanced punishment and reduce the impact of uncertainty.

Aggregation of heterogeneous distributed energy resources (DERs) and prosumers for profit maximisation by virtue of the market interface has evolved into virtual power plants (VPPs). The VPP operator optimises its aggregated resources to maximise profit by participating in the wholesale electricity market. VPP resources may be geographically dispersed and connected to a transmission grid at various nodes having different locational marginal prices (LMPs). These LMPs vary throughout the day and exhibit mutual correlation. Considering this, the current study presents a profit maximisation framework for VPP operators working under multiple LMPs, with their correlated uncertainties. This work also provides VPP resource scheduling for profit maximisation, considering optimal trading at various LMP nodes for a constrained internal network. This decision is devised using Markowitz's mean‐variance (M‐V) criterion under expedient correlation between volatile LMPs. A numerical case study on the proposed model with the PJM market demonstrates that LMP correlation alters the role of the VPP operator. This affects its trading and scheduling decisions as a buyer/seller at various time intervals to improve its profit–risk trade‐off and consequently the market interfacing of the VPP operator. The proposed model is relevant in practical market conditions for day‐ahead/medium‐term VPP planning while managing market uncertainty.

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.

A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

Virtual power plant (VPP) amalgamates diverse distributed resources, thereby unlocking the full potential of distributed energy’s dispatch capabilities. Energy storage is an effective means to address the uncertainty of renewable energy and achieve energy complementarity. This study meticulously evaluates the finite nature of traditional energy within VPP, the unpredictability associated with renewable energy outputs, and the constraints faced by energy storage devices. It introduces strategic operational frameworks for VPP through cooperation between VPP operator and energy storage provider managing new type of electric energy storage systems. With the dual objectives of amplifying the economic gains for VPP operator and maximizing benefits for energy storage provider, this research formulates a VPP economic low-carbon dispatch model, guided by a carbon tax framework. The improved particle swarm optimization algorithm is used to solve the proposed dispatch model. The numerical results confirmed that the collaboration between the VPP operator and the energy storage provider mitigates the issues caused by the variability of renewable energy outputs on the VPP, resulting in a mutually beneficial scenario for both the operator and the storage provider. Additionally, carbon emissions have been drastically decreased in response to low carbon targets.

The development of renewable energy sources (RESs) is a key element of the energy policy in Poland and the European Union. The transition to green energy aims to reduce greenhouse gas emissions, enhance energy security, and decrease dependence on fossil fuels. In Poland, the RES sector, particularly photovoltaics and wind energy, is growing, which is changing the operation of energy generation. However, the increasing share of RESs in the energy mix presents challenges for the stability of the national power grid. This study focuses on renewable energy sources in Poland because the development of RESs is crucial for the country’s energy transition. Poland is striving to achieve its climate goals and reduce dependence on fossil fuels, and increasing the share of RESs in the national energy mix is a key element of energy policy. The transition to green energy aims to reduce greenhouse gas emissions, enhance energy security, and support sustainable development in Poland. (1) The aim of this article was to analyze the impact of RES development on power grids in Poland, identify key issues, and review adaptation strategies. (2) Methods such as a literature review and statistical data analysis were used to build scenarios. (3) In Poland, the RES sector is being developed through the application of both national and European policies. The main sources of renewable energy are wind energy and photovoltaics. (4) The introduction of technologies such as energy storage systems, smart grids, and advanced management strategies is a key response to the challenges posed by the development of renewable energy in Poland, particularly in relation to the stability of the power grid.

The transition towards zero-carbon energy production is necessary to limit global warming. Smart energy systems have facilitated the control of demand-side resources to maintain the stability of the power grid and to provide balancing power for increasing renewable energy production. Virtual power plants are examples of demand–response solutions, which may also enable greenhouse gas (GHG) emission reductions due to the lower need for fossil-based balancing energy in the grid and the increased share of renewables. The aim of this study is to show how potential GHG emission reductions can be assessed through the carbon handprint approach for a virtual power plant (VPP) in a grid balancing market in Finland. According to our results, VPP can reduce the hourly based GHG emissions in the studied Finnish grid systems compared with the balancing power without the VPP. Typical energy sources used for the balance power are hydropower and fossil fuels. The reduction potential of GHG emissions varies from 68% to 98% depending on the share of the used energy source for the power balancing, thus VPPs have the potential to significantly reduce GHG emissions of electricity production and hence help mitigate climate change.

Multi-agent approach to modeling and simulation of microgrid operation with vehicle-to-grid system

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

A virtual power plant (VPP) can realize the aggregation of distributed generation in a certain region, and represent distributed generation to participate in the power market of the main grid. With the expansion of VPPs and ever-growing heat demand of consumers, managing the effect of fluctuations in the amount of available renewable resources on the operation of VPPs and maintaining an economical supply of electric power and heat energy to users have been important issues. This paper proposes the allocation of an electric boiler to realize wind power directly converted for supplying heat, which can not only overcome the limitation of heat output from a combined heat and power (CHP) unit, but also reduce carbon emissions from a VPP. After the electric boiler is considered in the VPP operation model of the combined heat and power system, a multi-objective model is built, which includes the costs of carbon emissions, total operation of the VPP and the electricity traded between the VPP and the main grid. The model is solved by the CPLEX package using the fuzzy membership function in Matlab, and a case study is presented. The power output of each unit in the case study is analyzed under four scenarios. The results show that after carbon emission is taken into account, the output of low carbon units is significantly increased, and the allocation of an electric boiler can facilitate the maximum absorption of renewable energy, which also reduces carbon emissions from the VPP.