Virtual Power Plant Operation Research Articles

Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building’s heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model’s goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system.

Trading strategy for virtual power plant clusters based on a multi-subject game model

With the rapid development of renewable energy and the continuous growth of new loads, VPP has become an important form of smart grid and energy internet due to its flexible and effective management of distributed energy. During the operation of Virtual power plant, there is a game relationship between the system operator and VPP, and they are in a non-complete information environment. However, most of the current game optimization modeling is under the condition of complete information, and the game model based on complete information cannot solve this problem. This article focuses on the VPP cluster trading problem based on non-complete information game theory, constructs a Bayesian game model for multiple VPPs with multiple subjects under the master-slave game framework by introducing the Bayesian concept to optimize the cluster transactions within VPPs, and verifies the effectiveness of the model through simulation experiments. The experimental results show that the multi-VPP multi-subject Bayesian game model established in the study can guarantee the privacy of each subject and effectively reduce PAR, thus ensuring the security and stability of the VPP network and reducing cost expenditures, which has practicality in actual VPP cluster transactions.

Research on multi-market strategies for virtual power plants with hydrogen energy storage

As the main body of resource aggregation, Virtual Power Plant (VPP) not only needs to participate in the external energy market but also needs to optimize the management of internal resources. Different from other energy storage, hydrogen energy storage systems can participate in the hydrogen market in addition to assuming the backup supplementary function of electric energy. For the Virtual Power Plant Operator (VPPO), it needs to optimize the scheduling of internal resources and formulate bidding strategies for the electric-hydrogen market based on external market information. In this study, a two-stage model is constructed considering the internal and external interaction mechanism. The first stage model optimizes the operation of renewable energy, flexible load, extraction storage, and hydrogen energy storage system based on the complementary characteristics of internal resources; the second stage model optimizes the bidding strategy to maximize the total revenue of the electricity energy market, auxiliary service market and hydrogen market. Finally, a typical scenario is constructed and the rationality and effectiveness of the strategy are verified. The results show that the hybrid VPP with hydrogen storage has better economic benefits, resource benefits and reliability.

Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms

The wholesale electricity market is composed of real-time market and procurement. Since the fully liberalization of the energy market in Singapore in 2018, competition among the market participants has become intensive. Therefore, the forecast of the electricity price, including both Uniform Singapore Energy Price (USEP) and regulation price (REG), is essential for market participants to trade at the best possible price. This study proposes an event-driven forecast model to provide a real-time automatical price forecast of the wholesale electricity price using the cutting-edge regression tree ensembled algorithm, XGBoost. The model is triggered every half of an hour by the dispatch of the latest electricity price, which will be also used as the input together with other necessary training data from the historical price database. The training is finished within 1–2 min and the model makes the predictions on prices of the next 6 h. A better prediction has been demonstrated benchmarked to the forecast provided commercially by Energy Market Company. The forecast model only takes commercially available data as the input and is embedded with messaging broker, enabling online collaboration of different modules. The model is readily implemented, and thus can serve as an effective supplementary tool in virtual power plant operation with a higher renewable energy penetration in the future.

Day-Ahead Bidding Strategy of a Virtual Power Plant with Multi-Level Electric Energy Interaction in China

Effective aggregation and rational allocation of flexible resources are the fundamental methods for solving the problem of an insufficient flexibility adjustment ability of a power system. The flexible scheduling resources of a distribution system are often small in scale and distributed mostly by different stakeholders. A virtual power plant (VPP) gathers small resources to participate in the day-ahead electricity market, but, due to the scale and characteristics of a VPP’s internal flexible resources, it cannot reach the access threshold of a peak shaving market in some periods due to small differences. In order to solve the market bidding problem of a VPP limited by capacity, and to achieve economic goals, a virtual power plant operator (VPPO) not only needs to interact with internal subjects but also needs to interact with other subjects with flexible resources in the distribution network. In this study, an electric vehicle (EV) cluster is taken as the interactive object, and a day-ahead bidding strategy of a VPP with multi-level electric energy interaction is proposed. The VPP not only makes full-time game pricing for internal participants but also makes time-sharing bargaining with an EV operator. The validity and the rationality of the proposed strategy are verified by an example.

Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market.

Energy Storage System Owner as a New Player in an Electricity Structure

Daily load demand causes different marginal cost at each hour due to different operation generation units. During peak load period, expensive generation units are determined to be turned on to provide sufficient electricity supply and sufficient spinning reserve. During light load period, generation units could not be unloaded due to their minimum up/down time, startup time and startup cost, causing uneconomic operation of the generating capacities. This excess capacity during light load period can be stored in the energy storage system (ESS) and the power can be released to supply the peak load demand hours to avoid turning on the next expensive unit which resulted in higher marginal cost. Performing as virtual power plant (VPP), ESS owners can seek for market opportunities to enter electric supply industry. This paper aims for proposing an optimal operation of VPP with charging/discharging ESS plan when marginal cost identifies real-time pricing (RTP) at each hour to be used as buying/selling price to VPP. Under strategically charging/discharging scheme, both parties, i.e., utility, and VPP, can achieve benefits in terms of better economic operation, lower system generation cost, increase operational income, while environmental impact is considered. The proposed method is tested on a ten-unit system under a centralized power market structure. Numerical results show that appropriate charging/discharging strategy could provide lower total production cost and offer opportunities for ESS owners as VPPs to obtain arbitrage marginal cost.

Technical and economic operation of VPPs based on competitive bi–level negotiations

In recent years, because of the shortage of fossil fuels and their consequent price increase, along with the environmental pollution associated with these types of fuel, the use of renewable energy resources has increased considerably. Moreover, regarding the technical and economic benefits of the use of distributed energy resources (DERs), these resources play an important role in the electricity market. Virtual power plants (VPPs) are decentralized energy management systems that participate as independent units in the electricity market by collecting the capacity of distributed energy resources, including distributed generation units, storages, and interruptible loads. In this paper, the VPPs collect DERs and use them to participate in the power retail market and assist in meeting the forecasted demand of the distribution network. The main goal is optimal pricing of produced energy of VPPs for long-term Bi–level negotiations with the utility. To reach the goal, two different conditions are considered. In the first mode, there is no competition among VPPs over offering price to utility and cooperation in providing load. In this mode, the total benefit of VPPs is optimized, and they offer their prices to utilities based on energy market prices in upstream networks. In the second state, the competition among VPPs is modeled by using game theory. Then, the optimization is done separately for all VPPs, and the criterion for offering the price for each VPP is the energy market price in upstream networks and also the suggested prices of neighboring VPPs. In both modes, the utility, as the only owner and operator of the distribution network, evaluates the suggested energy price of VPPs with the benefit obtained from the dispatch of these power plants and decides on the amount and the time that it must dispatch from distributed generation units of these power plants. The results show that, by applying the proposed method in the competition mode, the profit of both utility and VPPs has enhanced.

The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

Urban virtual power plant operation optimization with incentive-based demand response

Urban virtual power plant (VPP) shows great potential in alleviating urban power shortage or power supply-demand imbalance. This study proposes a bi-layer optimization model considering incentive-based demand response (IDR) to optimize the operation scheduling of urban VPP. The bi-layer optimization model includes the lower-layer IDR model and the upper-layer urban VPP optimal operation scheduling model. In the lower-layer IDR model, user satisfaction, comfort and preference are considered. Users mainly participate in the scheduling of urban VPP by IDR. The demand response resources obtained by urban VPP through providing incentives for users can effectively alleviate the energy supply pressure. In the upper-layer urban VPP optimal operation scheduling model, the scheduling plans for generating units and trading plans for the electricity wholesale market can be determined. Through upper-layer scheduling, it can not only meet the energy demand of users, but also minimize the urban VPP operation cost. The experimental results show that the proposed bi-layer optimization model for urban VPP operation considering IDR can achieve urban energy resources optimal allocation and support urban energy supply-demand balance, on the premise of ensuring economic benefits.

Optimal configuration method of electric vehicle’s participating in Load Aggregator’s VPP low-carbon economy

This paper aims to improve the reliability of multi-type load power supply, reduce the disorder of electric vehicle (EV) charging, and ensure the low-carbon economic operation of virtual power plant (VPP). Therefore, the optimal allocation method of low-carbon economy for VPP involving load aggregator (LA) participated by EV is proposed. First, a probabilistic model of electric load demand of a VPP is constructed, and an aggregation model of multiple types of uncertain loads is established based on the probabilistic model. Secondly, the disorder of EV charging is analyzed. Considering the time-of-use price policy, an optimization model with the maximum profit of load aggregator is established. Finally, the supply and demand matching model of VPP is established, and the minimum carbon emission and maximum net income of virtual power plant are optimized. Through simulation analysis, it is verified that this optimal configuration method can effectively improve the economics of EVLA and VPP, and ensure low-carbon operation of VPP.

Electric Water Heater Modeling for Large-Scale Distribution Power Systems Studies with Energy Storage CTA-2045 Based VPP and CVR

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.

Integrated community energy and harvesting systems: A climate action strategy for cold climates

In this article, the Integrated Community Energy and Harvesting System is introduced, a grid modernization solution for cold climates that incites a paradigm shift in virtual power plant design and operation. The focus is on system-wide GHG reductions by using variable temperature micro-thermal networks and prioritizing the harvesting of existing residual (waste) energy resources in communities, such as high-grade heat from decentralized fossil-fuel marginal generators, low-grade heat from cooling processes and curtailed carbon-free electricity. The novel strategy enables rapid fuel switching between residual energy resources, changing the micro-thermal network temperature between 20 and 70 °C on the scale of an hour, which provides valuable electrical demand response while maximizing the use of existing underutilized energy resources. Thermal storage is shown to have a critical role in both storing residual energy for later use, daily and seasonally, and enabling electrical demand response by rapidly changing the micro-thermal network temperature. The quantity of residual energy sources identified highlights that, as much as, 50% of all building heating loads could be met by energy currently rejected to the atmosphere. To illustrate the ICE-Harvest system’s effectiveness, a detailed case study is conducted on a typical integrated community and then applied to 1,000 prospective sites across a cold climate jurisdiction with a relatively low-carbon grid. It is shown that cooling process heat recovery, an energy source which is already located at buildings, can provide 24% of the prospective sites’ heating load when powered by carbon free, otherwise curtailed electricity. The results demonstrate that mass deployment of ICE-Harvest systems has the potential to provide 72% of the heating demand of these building clusters from residual energy sources, corresponding to an over 58% reduction in GHG emissions.

Stochastic trading of storage systems in short term electricity markets considering intraday demand response market

In recent years, the renewable energy sources are used in the power system for reducing environmental pollution. One of these renewable energy sources is wind farm that is used widely. However, they increase the uncertainty of the power system. To solve this problem, plenty researches have proposed several methods that have some disadvantages. This paper tries to decline the effects of uncertainty of wind turbines by using of the energy storage systems and optimal demand response programs. A stochastic planning of energy storage systems and wind generation are presented in the short term electricity markets considering the trading day-head and intraday market of demand response. In order to formulate an accurate model of various components of system, all needed constraints are considered. In this problem, wind turbines, energy storage systems, and demand response aggregator work coordinately that is called virtual power plant. For better comparison, two states consist of coordinated and uncoordinated working of all components are considered and assessed. The various scenarios for electricity market price, adjustment market price, imbalance energy price, and the generated power of wind turbine are considered. These scenarios are generated with hybrid neural network and coati optimization algorithm. Also, the iterative fast progressive Kantorovich method is used for scenario reduction. The results show the better response of the proposed method in optimal operation of virtual power plant.

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Optimal day-ahead scheduling of renewable energy-based virtual power plant considering electrical, thermal and cooling energy

For synchronized functioning of distributed renewable energy resources, virtual power plant (VPP) is manifested as an upshot for maximizing net profit of the partakers taking into account uncertainties. Here, optimal day-ahead scheduling for VPP has been performed for meeting electrical, thermal and cooling demands. VPP comprises small hydro power plants, wind turbine generators, solar micro-cogeneration units, biomass-fuel-fired micro-cogeneration units, battery energy storage system, plug-in electric vehicles, electric chiller, absorption chiller, ice storage conditioner and thermal energy storage system. SMC units and BMC units are incorporated alternately. Demand response program is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost. The scheduling problem is solved by utilizing bottlenose dolphin optimizer (BDO), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf (GWO) optimization so as to maximize net profit of VPP operator. The maximum profit obtained from BDO is about 5.25 % and 9.82 % more than the maximum profit obtained from HPSO-TVAC and GWO respectively.

Improving flexible optimal scheduling of virtual power plants considering dynamic line rating and flexible supply and demand

Global warming, air pollution, and the need for energy has led to the growth of renewable energy sources (RESs) around the world. Despite the fact RESs offer less expensive energy to customers, uncertainty in their output power, along with other sources of uncertainty inherent in power systems operations such as market price and load forecast, creates an imbalance between demand and supply. The virtual power plant (VPP) has been recognized as a solution that can integrate RESs into the power system to help deal with uncertainty. VPP operators require a flexible power system to address the supply and demand imbalance by rescheduling energy resources. In this study, coordinated scheduling of flexible resources in the form of a VPP participating in a day-ahead (DA) market is presented. Flexible resources and components include fast ramping conventional generators (CGs), battery energy storage systems, dispatchable wind turbines equipped with compressed air tanks, lines equipped with dynamic line rating (DLR) technology, and a flexible demand response program. In addition, flexibility indices (FI) are provided for each flexible component to capture each component’s impact on system flexibility. The proposed model is a mixed integer linear program (MILP) implemented on the IEEE standard 33 bus system. The result of the study show that the system equipped with DLR technology increases VPP profit when compared to the system with static line rating by 23%. The 24-hour average system’s FI indicated an increase in operational flexibility by the proposed model. Finally, the energy generated by each flexible resource is compared for different cases. The results indicates an increase in power sold to the upstream network by the VPP and a decrease in the need for load shedding.

Pricing Strategy for a Virtual Power Plant Operator with Electric Vehicle Users Based on the Stackelberg Game

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator’s bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources’ contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue.

Portfolio optimization of generic energy storage-based virtual power plant under decision-dependent uncertainties

Distributed energy resources (DERs) can be integrated into a smart and aggregated entity, namely a virtual power plant (VPP). This integration is beneficial to facilitate their flexibility utilization and optimally allocate their capacity to participate in competitive electricity markets. While various types of uncertainties exist in the market operation of VPP, such as the stochastic power of renewable energy resources (RES), etc., the literature generally overlooked the decision-dependent uncertainties (DDUs) with the operation of generic energy storage (GES). To be risk-hedging, this manuscript proposes a novel approach for risk-averse portfolio optimization of VPP with special attention to DDUs. To achieve tractable optimization, we propose a two-stage chance-constrained stochastic optimization model and three reformulations of different types of uncertainties. Furthermore, a modified and iterative progressive hedging algorithm is further proposed to handle the conditional value at risk (CVaR) and DDU items and efficiently solve the problem with large-scale GES portfolios. Exhaustive case studies based on real-world data demonstrate that VPP has to face the consequence of a significant real-time penalty (6.36 % loss in average profit) and losses in GES power (about 6.5 MW) when overlooking the DDUs' consequences. Moreover, it is more beneficial for greedy decision-makers to incorporate the DDUs' impact into their less risk-aversion strategy. Additionally, comparative results prove the validity of the proposed algorithm and its superiority in solving problems with large-scale GES portfolios and high-performance computing systems (over 30 % CPU time saved).

A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

With the goal of pursuing carbon neutrality, this study is aimed to investigate effectively managing distributed renewable energy. Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES). Time–space distribution characteristics of EVs and the responsiveness of EV users are analyzed and the available capacity calculation model is constructed. In the first stage, the bidding strategy and unit combination of the VPP are determined based on the predicted values of WP, PV, and load. Combining with the pre-decided decision from the first stage, the output of each unit is determined by considering an uncertainty set in the second stage, for achieving the best operational benefit under worst-case scenarios. Robust control parameters are introduced into the model and used to adjust conservativeness. The model is solved by applying the C&CG algorithm. Additionally, a carbon trading mechanism is leveraged to balance the system economy and environmental friendliness. The results show that the optimal scheduling solution helps to reduce fluctuations caused by uncertainties, balancing the economic benefits and operational risks of the VPP. Besides, guidance is provided for VPP operators to formulate reasonable incentives for EV users and the construction of energy storage systems. When the carbon trading price of the VPP proposed in the paper is set at 160 ¥/ton, economic and environmental sustainability can be effectively balanced.