Combined Power Plant Research Articles

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.

Two-Stage Optimization Scheduling of Virtual Power Plants Considering a User-Virtual Power Plant-Equipment Alliance Game

Distributed renewable energy, loads, and power sources can be aggregated into virtual power plants (VPPs) to participate in energy market transactions and generate additional revenue. In order to better coordinate the transaction relationships among various entities within VPPs, this paper proposes a two-stage optimization model for VPPs that considers the user-VPP-equipment alliance. Firstly, starting from the basic structure of VPP, it is proposed to divide the alliances in VPP into two alliances: demand-side user-VPP and supply-side equipment-VPP. And a VPP optimization framework considering the cooperative game of the user-VPP-equipment alliance has been established. Then, a two-stage optimization model for VPPs was established considering the cooperative game of user-VPP-equipment alliance. The day-ahead optimization model takes economic and social benefits as the dual objectives, and the intraday optimization model aims to minimize the cost of deviation penalties. Secondly, taking into account the risk levels and comprehensive marginal benefits of various entities within the VPP, a profit distribution method combining improved Shapley values and independent risk contribution theory is adopted to allocate the total revenue of the VPP. The case results show that the operating cost has been reduced by 5.75%, the environmental cost has been reduced by 4.46%, and the total profit has increased by 29.52%. The model can improve the overall efficiency of VPPs.

Flexible resource dynamic aggregation regulation method of virtual power plant to ensure more renewable energy generation

The development of large-scale sustainable energy has affected the security of electricity systems. Virtual power plant (VPP) realize multi-energy synergistic complementation and efficient operation mainly with electricity. The paper proposed a flexible resource dynamic aggregation regulation method of VPP to ensure more renewable energy generation, which enables the efficient operation of multiple energy sources with electricity as the main source through collaborative and complementary measures. Firstly, the operating mode of VPP under market mechanisms is proposed, and the operating mechanism for VPP's participation in the market is established. Secondly, the types of flexible resources are identified, a cooperative game relationship between multiple autonomous agents within the power grid and VPP is formed to support an intelligent control method for VPP considering the complementarity of multiple energy sources and carbon emissions. Finally, a novel inverse cotangent compound differential evolution (NICCDE) algorithm is proposed by combining the innovative composite differential evolution algorithm with the tangent function, which realizes the flexible synergistic utilization of multiple energy sources. and reduces the training cost of the algorithm. Case studies from the conducted case study demonstrate the significant cost reduction achieved by the proposed method in comparison to conventional approaches when applied to VPP. Moreover, the utilization of this method enhances the precision and comprehensiveness of the search outcomes, thereby augmenting the overall effectiveness of the VPP system.

DISTRIBUTED MICROGRIDS USING CRYOGENIC STORAGE SYSTEMS OF ELECTRICITY PRODUCED BY RENEWABLE SOURCES AS AN IMPORTANT ECOLOGICAL AND ECONOMIC FACTOR

The paper shows that the use of cryogenic technologies for the storage of electrical energy produced by renewable sources makes it possible to move from centralized energy supply systems to microgrids based on distributed energy systems. Also, cryogenic technologies make it possible to switch from centralized gas supply to individual supply of residential buildings and industrial enterprises with liquefied natural gas. It is expedient to create in Ukraine, on the basis of cryogenic storage systems of electricity produced by renewable sources, already existing local systems of electric power generation of various types, distributed systems of generation, united in micro-grids and virtual power plants. It is also possible to create virtual heating systems. Such technologies meet the requirements of the European Union for candidate countries to join the Union and open the possibility of financing projects related to the restoration of the energy system of Ukraine based on renewable sources and advanced energy-efficient technologies. The proposed scheme for obtaining liquid air and placing cryogenic equipment allows us to talk about the creation of a new sector of cryogenic energy in Ukraine. Bibl. 14, Fig. 2, Tab. 2.

SREM: Smart renewable energy management scheme with distributed learning and EV network

In this article, aiming to develop the Green Internet of Vehicles (G‐IoV), we propose a smart energy management system that leverages the intelligence edge clients and the distributed electric vehicles (EVs). The system proposed in this article incorporates the benefits of both software, specifically in terms of the user interface, and hardware, specifically in terms of edge clients. In particular, this system integrates intelligence edge clients with an EV CAN bus network as an electronic control unit. By leveraging the intelligent edge clients recommendation system, EVs can make informed decisions on battery charging or discharging actions. As a result, a virtual‐power‐plant (VPP) can treat the EVs network as a vast intelligent energy storage facility, efficiently managing the battery energy of all distributed EVs connected to the platform and fully utilizing the electricity generated from renewable energy sources. We experimentally verify that using federal learning to train models in EV networks versus training models directly in EVs, using federal learning in EV networks yields better experimental results.

CONVERSION OF ANTHRACITE BOILER UNITS OF CHP PLANTS FOR COMBUSTION OF SUB-BITUMINOUS COAL IN THE WAR CONDITIONS

The work describes the task of urgently converting the boilers of anthracite-burning combined heat and power plants to sub-bituminous coal, which arose in the conditions of Russian aggression in connection with the complete cessation of supplies of project fuel. The developed methods of solving it in a short time with the maximum use of available equipment are given, using the example of boilers with a steam capacity of 220 t/h of CHPP «EURO-RECONSTRUKTSIA» LLC (Darnytska CHPP, Kyiv). On the basis of calculation justification it is shown, and the tests and operation have confirmed, the safety and effectiveness of the technical solution with preservation of coal drying by hot air while limiting the temperature at the exit of the mill and with pulverized coal transport to the burners by half-heated air taken after the first stage of the air heater, while reducing the diameter of the pulverized coal pipes. Measures are proposed and implemented to equalize the distribution of pulverized coal and air among the burners, to operate the mills in alternating mode with the aim to increase the use of hot air, correspondingly to increase the coal jet temperature, to improve the conditions of molten slag removal and to reduce the temperature of flue gases, etc. Uninterrupted operation of the Darnytska CHPP became an important factor in ensuring the functioning of the critical infrastructure of heat and energy supply of Kyiv in conditions of enemy shelling. Bibl. 18, Fig. 5, Tab. 4.

Multi-objective battery energy storage optimization for virtual power plant applications

The increasing share of renewable energy sources (RESs) in electricity generation leads to increased uncertainty of generation, frequency and voltage regulation as well as difficulties in energy management. A virtual power plant (VPP), as a combination of dispersed generator units, controllable load and energy storage system (ESS), provides an efficient solution for energy management and scheduling, so as to reduce the cost and network impact caused by the load spikes. This paper proposes a multi-objective optimization (MOO) of battery energy storage system (BESS) for VPP applications. A low-voltage (LV) network in Alice Springs (Northern Territory, Australia) is considered as the test network for this study. The BESS for each customer is used to store and release the energy when required to maintain the voltage regulation performance of the LV network and reduce the cost. To optimize the charge/discharge schedule in each battery, a multi-objective optimization tool (MOOT) is developed, where MOO can directly communicate with DIgSILENT PowerFactory platform to perform multi-time scale simulation. MOO mainly focuses on generating a set of trade-off schedule solutions for each customer over 24 h by considering the customer’s cost and network impact. Then according to the stakeholder’s prior preference, one of the solutions is selected and verified in DIgSILENT PowerFactory with a realistic LV network. We also design several scenarios with different penetrations of photovoltaics (PVs) and batteries to verify how they influence customer’s cost and LV network. With the increasing penetrations of PVs and batteries, the experimental results over the selected solution according to the customer preference show that the customers’ cost can be largely reduced by maintaining the network voltage regulation performance.

Strategic decision making of energy storage owned virtual power plant in day-ahead and intra-day markets

In this paper, a service-oriented aggregator like a virtual power plant is modeled and designed to facilitate the integration of distributed generations in both day-ahead and intra-day power markets and simultaneously collaborate with the distribution system operator to manage the grid utilization problem. To do this, a proper plan of interactions between power system and market operators, customers, and aggregators concerning participation in the electricity market is suggested and explained. The optimal arrangement of the real and reactive power flows of VPP sources is a solution used to manage the congestion of the distribution network with the aim of maximizing the integration of renewable energy into the energy market. The VPP utilizes the rolling horizon technique to decrease the uncertainties related to renewables in the scheduling horizon. To prevent unwarranted wind and solar power generation curtailments, VPP behaves flexibly and reschedules its resources according to the contracts that are concluded. In this work, a modified IEEE 33-bus distribution test system is utilized to investigate the efficiency and proficiency of the proposed method. The simulation and numerical results demonstrate that the suggested VPP model can not only improve solar and wind power integration in the energy markets but also alleviate congestion in the distribution networks.

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.

Adaptive primal–dual control for distributed energy resource management

With the increased adoption of distributed energy resources (DERs) in distribution networks, their coordinated control with a DER management system (DERMS) that provides grid services (e.g., voltage regulation, virtual power plant) is becoming more necessary. One particular type of DERMS using primal–dual control has recently been found to be very effective at providing multiple grid services among an aggregation of DERs; however, the main parameter, the primal–dual step size, must be manually tuned for the DERMS to be effective, which can take a considerable amount of engineering time and labor. To this end, we design a simple method that self-tunes the step size(s) and adapts it to changing system conditions. Additionally, it gives the DER management operator the ability to prioritize among possibly competing grid services. We evaluate the automatic tuning method on a simulation model of a real-world feeder in Colorado with data obtained from an electric utility. Through a variety of scenarios, we demonstrate that the DERMS with automatically and adaptively tuned step sizes provides higher-quality grid services than a DERMS with a manually tuned step size.

DECENTRALIZATION – THE MAIN WAY FOR THE TRANSFORMATION OF THE ENERGY INDUSTRY OF UKRAINE

Topicality. The events of November 2022 showed the absolute instability of Ukraine's centralized energy system. Up to 40% of the centralized energy infrastructure was disabled in two days. Millions of people were left without electricity, heat and water for a long time. Therefore, it is very important to develop the energy sector of the state in the future, taking into account the needs of Ukraine's decentralization policy, in particular, local communitiesAim and tasks. The purpose of the research is to determine the imperatives of decentralization of the energy sector of Ukraine. The main way of developing the energy system of the state is the transition from a centralized to a distributed system. The location of generation systems in united communities makes it possible to switch to systems of cogeneration of electric and thermal energy to increase the efficiency of installed capacities and save fuel significantly by up to 40%.Materials and Methods. The methodological basis for the formation of regional policy should become the best of the world and European practices of the country`s energy sector development. Active use of the methods of system analysis, expert assessment on the implementation of decentralization systems of the energy industry is a guarantee of achieving the priorities of national security of Ukraine in the post-war period. Priority support from the EU and others international donors refers to those projects and programs aimed at decentralization, decarbonization, local economic sustainability and energy independence.Research results. The research substantiates that local communities have great opportunities for using alternative renewable resources for the generation of electricity and thermal energy. First of all, it is biomass, biogas, solar energy, wind energy and geothermal energy. In particular, the authors proved that due to the installation of means of electric energy accumulation, it is possible to raise the level of use of the installed capacities of solar and wind power plants by another 50% and to bring the total level of use of the installed energy capacities to 70%.Conclusions. The active implementation of alternative energy sources into economic practice can become the basis of the national security of the state in the post-war period. It is suggested that Communities use nearby agricultural waste and solid household waste as fuel instead of imported fossil fuel. It is also proposed to create local electric networks combined into virtual power plants within the framework of united communities, including not only all types of generation available on the territory, but also systems of short-term and long-term storage of electric energy. For the long-term storage of large amounts of electrical energy, it is suggested to use accumulators based on cryogenic liquids. It has been proven that due to the installation of means of electric energy accumulation, it is possible to raise the level of use of the installed capacities of solar and wind power plants by another 50% and to bring the total level of use of the installed energy capacities to 70%. Virtual power plants make it possible to significantly increase the amount of generated (consumed) electricity without additional investment costs. Such technologies meet the requirements of the European Union for candidate countries to join the Union and open the possibility of financing projects related to the restoration of the energy system of Ukraine based on renewable sources and advanced energy-efficient technologies.

A Wasserstein‐distance‐based distributionally robust chance constrained bidding model for virtual power plant considering electricity‐carbon trading

AbstractVirtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach.

Supporting virtual power plants decision-making in complex urban environments using reinforcement learning

Virtual Power Plants (VPPs) are becoming popular for managing energy supply in urban environments with Distributed Energy Resources (DERs). However, decision-making for VPPs in such complex environments is challenging due to multiple uncertainties and complexities. This paper proposes an approach that optimizes decision-making for VPPs using Reinforcement Learning (RL) in urban environments with diverse supply-demand profiles and DERs. The approach addresses challenges related to integrating renewable energy sources and achieving energy efficiency. An RL-based VPP system is trained and tested under different scenarios, and a case study is conducted in a real-world urban environment. The proposed approach achieves multi-objective optimization by performing actions such as load-shifting, demand offsetting, and providing ancillary services in response to demand, renewable generators, and market signals. The study validates the effectiveness and robustness of the proposed approach under complex environmental conditions. Results demonstrate that the approach provides optimized decisions in various urban environments with different available resources and supply-demand profiles. This paper contributes to understanding the use of RL in optimizing VPP decision-making and provides valuable insights for policymakers and practitioners in sustainable and resilient cities.

Abnormal Detection Using Two-Stage Method in Combined Power Plant

Complex systems, such as power plants, demand precise and reliable anomaly detection mechanisms. Traditional supervised learning approaches often fall short due to the challenges of imbalanced data and the scarcity of labeled abnormal instances. This paper introduces a two-stage methodology to address these challenges. The first stage emphasizes feature engineering, mitigating redundant sensor effects, and reducing dimensionality through Kmeans clustering and PCA. The second stage employs an LSTM-Autoencoder for abnormal event detection. Validated using data from a combined power plant, our approach demonstrates superior performance over existing techniques in terms of accuracy and computational efficiency. This research not only advances the field of anomaly detection in power plants but also offers insights for other complex systems.

Carbon capture from combined heat and power plants – Impact on the supply and cost of electricity and district heating in cities

The capture and storage of biogenic CO2 emissions from large point sources, such as biomass-combusting combined heat and power (CHP) plants, can contribute to climate change mitigation and provide carbon-negative electricity while supplying district heating in urban areas. This work investigates the impact of retrofitting CO2 capture processes to CHP plants in a city energy system context. An energy system optimization model is applied to a case study of the city Västerås, Sweden, with scenarios involving two existing CHP plants in the city, retrofitted with either a heat-driven (MEA) or an electricity-driven (HPC) carbon capture process. The results show that the CHP plants might be retrofitted with either option without significantly impacting the district heating system operation or the marginal costs of electricity and district heating in the city. The MEA process mainly causes a reduction in district heating output (up to 30% decrease on an annual basis), which can be offset by heat recovery from the capture unit. The electrified HPC process does not impact the CHP plant steam cycle but implies increased import of electricity to the city (up to 44% increase annually) compared to a reference scenario.

A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services

Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a management system for a residential virtual power plant that includes household loads, photovoltaic generation, energy storage systems, and electric vehicles. With the proposed management system, the virtual power plant is economically optimized (as in commercial virtual power plants) while providing ancillary services (as in technical virtual power plants) to the distribution grid. A genetic algorithm with appropriate constraints is designed and tested to manage the energy storage system and the charge/discharge of electric vehicles, with several economic and technical objectives. Single-objective optimization techniques are compared to multi-objective ones to show that the former perform better in the studied scenarios. A deterministic gradient-based optimization method is also used to validate the performance of the genetic algorithm. The results show that these technical targets (usually reserved for larger virtual power plants) and economic targets can be easily managed in restricted-sized virtual power plants.

Update Scheduling for ADMM-Based Energy Sharing in Virtual Power Plants Considering Massive Prosumer Access

With the proliferation of distributed energy resources (DERs), electricity consumers in virtual power plants (VPPs) are transitioning into prosumers and are encouraged to share surplus energy with peers. Nevertheless, large-scale energy sharing among thousands of prosumers may encounter communication-related challenges. Communication network congestion may result in a significant increase in the negotiation waiting time to reach a sharing agreement, and potentially risks exceeding the deadline of negotiation before the market gate closes, rendering energy sharing ineffective. This paper proposes an online partial-update algorithm for the alternating direction method of multipliers (ADMM)-based energy sharing. By restricting the update connection between the VPP and the prosumers, the algorithm selects a subset of the prosumers participating in ADMM updates each round, hence eliminating the excessively long waiting time caused by communication congestion. Considering the delay induced by massive prosumer communication access requests, a method for determining the optimal number of prosumers participating in updates is provided. To fully utilize the limited update opportunities, a fair and efficient prosumer update scheduling policy is designed. The VPP schedules the participation of prosumers in updates such that the convergence-critical prosumers receive higher priority, yet every prosumer is granted sufficient update opportunities. Additionally, the extra computation and communication overheads brought by the prosumer scheduling are minimized, allowing the whole algorithm to be executed in real time. Numerical studies are conducted to validate the effectiveness of the algorithm and its performance in reducing the overall convergence time.

Optimal Operation Strategy for Multi-Energy Microgrid Participating in Auxiliary Service

Since multi-energy microgrid (MEMG) can coordinate various resources to operate as a virtual power plant (VPP), it is an important way to maintain the stable and economic operation of the power systems and decrease the impact of intermittence of distributed energy resources (DERs). However, the potential of MEMG as a VPP has not been thoroughly explored since auxiliary service (AS) market is not fully open for MEMG at present. The relevant challenges include balancing conflict of interests among multiple energy entities, motivating users to adjust flexible loads, integrating multiple flexible resources in energy supply/demand sides and formulating specific policies, etc. To handle these tasks, an optimal operation strategy for MEMG participating in AS is proposed by considering Stackelberg game theory and integrated demand response (IDR). The feasibility of the proposed strategy is validated by a practical MEMG in Hunan, China. The results show that the economic benefits of energy entities are effectively raised and the peak-shaving AS is realized while user satisfaction is also maintained. This work would give reference to the constructor of future AS market to formulate polices about the operation modes and pricing schemes of MEMG.

Virtual power plant optimal operation considering renewable energy uncertainty

An optimal operation model for a virtual power plant (VPP) is developed to promote the decarbonization of electricity as well as to enable the efficient grid integration of distributed power sources and electric vehicles. The model considers the uncertainty of renewable energy and the environmental punishment of gas turbines to achieve coordinated optimization of distributed resources within the VPP. The case demonstrates the utility of the model.

Two-stage dispatch optimal model of virtual power plant considering pumped storage

When virtual power plant (VPP) participates in power grid dispatch, it not only faces the problem of market bidding but also has the security and stability problems of voltage, frequency, power flow, and so on. The optimal dispatch strategy of VPP, which participates in a hybrid market, is proposed. The two-stage optimal model is established. An economic dispatch optimal model based on robust control and stochastic linear programming is established to maximize the profit of VPP. Aiming to minimize the difference between the output power of each distribution energy resource and the optimal results of economic dispatch, a safety dispatch optimal model based on a particle swarm optimization algorithm is established. The simulation results with the measured data of the Ningxia power grid show that the two-stage optimal dispatch model is effective.