Combined Power Plant Research Articles

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

Against the backdrop of China's carbon peak and carbon neutrality goals, the use of virtual power plants for electricity economic dispatch has gradually become a research hotspot. The virtual power plant includes both supply and demand sides. If the inherent conflicts of interest between both parties cannot be effectively addressed, there are significant hidden dangers in the sustainable operation of the virtual power plant. On the basis of existing research, this article constructs a regional virtual power plant. With the goal of maximizing supply and demand utility, a two-stage Stackelberg dynamic game model was established between virtual power plant operators and users in the electricity market and carbon trading market mechanisms, and an incentive based hierarchical carbon price constraint was designed in the optimization model. In the model constructed in this article, the operator of a virtual power plant will consider environmental benefits based on expected returns, adjust the output of controllable units to change the electricity supply strategy, and formulate a pricing strategy. At the same time, users will adjust their electricity consumption strategy based on their own electricity consumption characteristics, and both will achieve a game equilibrium. Based on the optimization results, this article conducted a sensitivity analysis on the incentive coefficient of graded carbon prices and obtained the optimal incentive coefficient under the case conditions. The research results indicate that the model can coordinate and meet the interests and needs of both the power generation and consumption sides, and the implementation of incentive based hierarchical carbon pricing strategies has certain advantages compared to traditional carbon pricing strategies in different user types scenarios. The research conclusion of this article has good reference value for the sustainable green operation of virtual power plants.

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

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

A novel surrogate polytope method for day-ahead virtual power plant scheduling with joint probabilistic constraints

Virtual Power Plant (VPP) is an emerging concept that can effectively manage a large number of distributed energy resources (DERs). However, the inherent uncertainty of load and renewable generation poses a challenge to reliable VPP power scheduling. This manuscript proposes a novel method for day-ahead VPP scheduling with a joint probabilistic guarantee on its power availability without violating the DER constraints and network constraints. A surrogate polytope is first used to find the inner approximation of the VPP power, implicitly including the low-level DER power, DER constraints, and network constraints. Then, a multivariate Gaussian distribution is used to fit the random parameters of the surrogate polytope, after which the iterative supporting hyperplane algorithm is used to solve the VPP scheduling problem. Extensive case studies based on real-world renewable generation scenarios demonstrate the superior performance of the proposed method in out-of-sample cost and reliability, with a manageable computing complexity.

Parameter Estimation Method for Virtual Power Plant Frequency Response Model Based on SLP

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a “centralized/decentralized” hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid.

Scheduling IDC-based virtual power plants considering backup power

The virtual power plant (VPP) can aggregate massive distributed resources to participate in power system operation. Different types of resources have been investigated to construct VPPs. Internet data centers (IDCs) are regarded as flexible power consumers that can potentially cooperate with power systems. This paper exhibits the insight for constructing VPPs based on IDCs while comprehensively considering the operational flexibility of the workloads and power supply devices. On the workloads, this paper models the temporal and spatial flexibility of workloads in detail. On the power supply devices, this paper explores their spare capacity with respect to the power supply reliability requirement. Then, a day-ahead scheduling method of the IDC-based VPPs is established based on the operational flexibility modeling. Case studies validate the proposed approach. Moreover, because of the coupling of workload dispatch and power supply device operation, the synergy effect does not equal the sum of its parts.

Comparative life cycle assessment of integrated renewable energy-based power systems

Integrated renewable-based power cycles should be employed to produce more sustainable electricity. This is a comparative life cycle assessment (LCA) of three combined power plants, encompassing: case 1 involving combined geothermal and wind, case 2 featuring combined geothermal and solar, and case 3 integrating wind and solar systems. The base case perovskite solar cell (PSC) modelling assumes a 3-year lifespan and a power conversion efficiency of 17 %. However, diverse scenarios are evaluated through a sensitivity assessment involving enhancements in lifetime and efficiency. The base case evaluation emphasizes that the phases with the most significant negative environmental effects which includes the drilling of geothermal wells, construction of wind plants, and manufacturing and installation of PSCs. The midpoint findings indicate that boosting the power conversion efficiency of PSC from 17 % to 35 % yields a notable decrease in environmental impact. Moreover, extending the lifetime from 3 to 15 years led to reduction in CO2 emissions from 0.0373 and 0.0185 kg CO2 eq/kWh to 0.026 and 0.0079 kg CO2 eq/kWh in cases 2 and 3, respectively. Assessing worst and best-case scenarios highlights significant declines in certain impact categories. In case 3, terrestrial ecotoxicity (TE), photochemical oxidant formation (POF), human toxicity (HT), marine ecotoxicity (ME), and marine eutrophication (MU) saw reductions exceeding 88 % compared to worst-case results. The environmental effects observed in cases 2 and 3 stem from toxicity and metal depletion, mainly linked to the PSC. Endpoint results revealed that when considering a PSC lifespan of 10 years or more, the detrimental ecosystem impacts of cases 2 and 3 become less severe than those of case 1. Uncertainty assessment has been done for different cases and impact categories. The study's results are also novel in which it evaluated the innovative PSC technology when integrated with other renewable resources, contrasting it with other integrated plants.

Characterization and application of flexible operation region of virtual power plant

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper.

Distributed differentially private energy management of virtual power plants

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP’s energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy for the VPP to maximize its profit by coordinating the prosumers and at the same time mitigate the privacy risks of local prosumers. Specifically, the coordination of prosumers is first formulated as an optimization problem, which includes the operation constraints of each individual prosumer and the VPP coordination process. On this basis, this paper proposes to solve the optimization problem with a two-level privacy protection strategy. For the individual level, a distributed solution framework is proposed to keep the private information of each prosumer preserved locally. For the communication level, a differential privacy mechanism is integrated into the information exchange process thus reducing the privacy leakage risk. Both theoretical and practical results are provided to verify the performance of the proposed method in terms of optimality and privacy protection levels.

Comparative Review of Thermal Management Systems for BESS

The integration of renewable energy sources necessitates effective thermal management of Battery Energy Storage Systems (BESS) to maintain grid stability. This study aims to address this need by examining various thermal management approaches for BESS, specifically within the context of Virtual Power Plants (VPP). It evaluates the effectiveness, safety features, reliability, cost-efficiency, and appropriateness of these systems for VPP applications. Among the various hybrid cooling options, two notably promising combinations are highlighted. First, the integration of heat pipes with phase change materials, which effectively conduct heat away from sources with minimal temperature differences, enabling swift heat transfer. Second, the combination of heat pipes with liquid passive cooling, which utilizes the efficient heat transfer properties of heat pipes and the steady cooling offered by liquid systems. This study offers recommendations for choosing the best thermal management system based on climate conditions and geographic location, thereby enhancing BESS performance and sustainability within VPPs.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Production of biobased polyethylene terephthalate and its precursors for diversification in the global sugarcane industry

The technical and economic viability of producing fully bio-based polyethylene terephthalate (PET) or its monomers was investigated. First generation (1 G) biorefineries, utilizing A-molasses, and combined first and second generation (1G2G) biorefineries, utilizing molasses and sugarcane bagasse and brown leaves, were considered. Simulations were developed for self-sufficient biorefineries, where the energy demands were met by utilizing the existing sugar mill boiler, accompanied by a new medium-pressure boiler and biogas generator. 1G2G scenarios required the replacement of the sugar mill boiler with a new, high-efficiency combined heat and power (CHP) plant, where electricity and useful heat were generated from a portion of 2 G biomass and other residues. 1 G PET was unprofitable at a minimum selling price (MSP) of $2946.t−1. 1 G production of Ethylene ($1847.t−1), monoethylene glycol (MEG) ($1648.t−1), 5-Hydroxymethylfurfural (HMF) ($1576.t−1) and iso-butanol (iButOH) ($1292.t−1) were more economically viable than PET, terephthalic acid (TPA) ($2689.t−1) and p-xylene (PX) ($2983.t−1), due to less complex processing and lower energy demands. 1G2G biorefineries were less profitable than 1 G equivalents, due to the cost of pretreatment and new CHP plants. The most profitable scenario was 1 G iButOH, with an MSP 19.0 % lower than its market price. New technologies to produce p-xylene and TPA from biomass in fewer steps, such as Virent Inc.’s Bioforming® technology, could potentially improve the economic viability of PET in the future.

Hierarchical robust Day-Ahead VPP and DSO coordination based on local market to enhance distribution network voltage stability

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

Data-driven virtual power plant aggregation method

Data-driven virtual power plant aggregation method

Integrating virtual power plants for sustainable supply chain operations in production-climate nexus

ABSTRACT This paper aims to design and integrate virtual power plant (VPP) into a geographically dispersed production-distribution network for attaining environmental sustainability goal. The VPP is comprised wind turbines, photovoltaics, combined heat and power, and energy storage units. The proposed VPP-based supply chain design model makes strategic allocations on renewable capacity as well as scheduling production, inventory, transportation, and energy generation and trading decisions. The model represents a first-of-its-kind in planning a multi-layer supply chain network in which renewable-based electric-thermal cogeneration is coordinated in production-climate nexus. The study yields the following findings. First, products with larger energy use opt to be made in locations with stronger sunshine or wind profile despite higher transportation costs. Second, the time-of-use rate greatly stimulates the installation of onsite photovoltaics and energy storage. Third, under flat utility rate, reducing energy storage cost has limited benefit to the cost savings of the supply chain operations.

Distribution network voltage control considering virtual power plants cooperative optimization with transactive energy

The integration of massive distributed energy resources (DERs) brings new challenges for the distribution networks (DNs) operation control. To control DERs effectively, virtual power plants (VPPs) are introduced into DNs. Conventionally, the control strategy of VPPs is formulated as an optimization model without considering voltage control and transactive energy (TE) in DNs. In this article, multiple VPPs cooperative optimization with TE is studied and cast as a nonlinear programming model. It aims to optimize the voltage control of DNs and the operation profits of VPPs. To dynamically capture the optimal operation state of VPPs in DNs, the distribution locational marginal pricing and DN partition are incorporated into the model. In general, this type of model is computationally challenging and mainly solved by approximate algorithms with low efficiency. Recently, multi-agent deep reinforcement learning (MADRL) is emerging as a scalable and promising data-driven approach. We propose an augmented MADRL solution to this problem. In the training stage, one soft actor-critic learning agent augmented by parameter sharing strategy is employed to simultaneously capture the state-action patterns of multiple VPPs. The prioritized experience replay technique is applied to improve learning efficiency and speed. Numerical test results demonstrate the superiority of the proposed method over benchmark methods.

P2P electricity trading model for urban multi-virtual power plants based on double-layer energy blockchain

The urban virtual power plant (VPP) enhances the overall performance of the urban power system by aggregating diverse distributed energy resources (DERs) to provide energy services. With the advancement of smart grid technology, peer-to-peer (P2P) electricity trading has emerged a new paradigm of electricity trading within urban VPPs. In this study, we propose a P2P electricity trading model for multi-VPPs in urban areas, based on a double-layer energy blockchain framework. Initially, users submit their electricity trading information for matching at the user layer; any remaining unfulfilled electricity demand is then further matched at the VPP layer. The user participation process in trading encompasses several stages: trading start, matching, validation, execution, and settlement. Additionally, this study introduces a double-layer blockchain structure consisting of one main chain and multiple side chains. The main chain records real-time trading information from the VPP layer while the side chains are dedicated to recording real-time trading information from the user layer. The feasibility of the proposed model is demonstrated through its implementation in Hyperledger Fabric. Experimental results indicate that the proposed model effectively reduces urban users' reliance on the main grid and decreases their electricity expenses.

A bi-level optimization framework for the power-side virtual power plant participating in day-ahead wholesale market as a price-maker considering uncertainty

This paper develops a novel bilevel decision-making framework for a power-side virtual power plant (VPP) comprising refined power to gas (P2G), wherein the upper-level is the cost minimization objective of VPP, while the lower-level is social cost minimization objective of wholesale electricity market. Meanwhile, wind power uncertainty is handled by distributionally robust optimization method. By utilizing Karush-Kuhn Tucker optimality condition and strong duality theory, the bilevel model is interpreted as a tractable single-level mixed integer programming model. Simulation results show that: relative to the models with removing uncertainty and refined P2G device, 1) the proposed approach realizes outstanding economic benefit by obtaining an increase with a rate by 7.47 % and 105.09 % in total actual profit, respectively. Regarding environmental benefit, CO2 emission volume experiences a 0.89 % decline and 0.90 % increase, and the CO2 intensity of the proposed strategy is the lowest with the value of 193. 25kg/MWh. 2) The proposed model can achieve the economic and low-carbon dispatching of the VPP. Wind power curtailment in the proposed model witnesses a trivial increase and reduced by 53.19 %, respectively. 3) The model exhibits merits due to its trade-off between robustness and computational complexity.

On the economics of co-opting in energy sector

The purpose of the work is to formulate the main provisions of the co-opting economy and detail them using the case method in the field of energy. Elizabeth Watkins and David Stark from Columbia University (USA) put forward the concept of Möbius organizational forms. Unlike traditional forms in business, Möbius forms do not Make, Buy or Cooperate, they Co-opt assets that do not belong to them. In 2019, the author of this work proposed the term "paraphernalia", which reflects the essence of the theory, but the name is more suitable for the Global Informality Project online encyclopedia, in which it was published. The co-opting economics is a social science about the production, distribution, exchange and consumption of goods and services with non-violent, but non-cooperative appropriation of assets belonging to others to satisfy one's own needs. The original formula of the co-opting economy is: money-commodity-money with a dash and an additional commodity (good) received for free, which is produced with the help of assets belonging to other owners. Theoretical studies were carried out using the case method. The effect of the co-opting economy is demonstrated on the example of the Sukha Balka mine (Kryvyi Rih). The mine has shafts hoists. In the process of moving loads, hoist generate electricity that is recovered and fed back into the network. The author introduces the conditional category "Mechanics", whose asset is mine hoist, and the category "Energeticians", whose asset is power distribution networks. The additional energy resource received by the Mechanics flows through the network to the Energeticians and becomes their property. There is a co-optation of assets that do not belong to Energeticians, with the receipt of a product that they use to meet their own needs. Such a virtual power plant does not belong to any energy company, but helps it to overcome the shortage of electricity during times of maximum load. Here, too, energy companies adhere to the economy of co-optation, because the generating assets they use are not their property. By adopting legislative acts on the introduction of the active consumer category, the state indirectly contributes to the development of the co-optation economy.

Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

The risks of electrified district heating in Finland's cold climate

Decarbonizing fossil fuel-dependent district heating systems is essential for achieving carbon neutrality, particularly in cold climates. In Finland, district heating operators are concentrating on electrifying these systems. However, the 2022 energy crisis in Europe has highlighted concerns about heat production costs and the security of heat supply with this approach. This study examines the economic feasibility and risks associated with electrified district heating systems and the early decommissioning of thermal power plants in the interconnected district heating systems of Helsinki, Espoo, and Vantaa. The case study is simulated and optimized to find the least-cost solution while meeting heat demand for various 2025 scenarios, assuming high energy market prices as in 2022 and more normal circumstances. Simulation results indicate that shutting down fossil fuel-based combined heat and power plants in Helsinki and Espoo would create a shortfall in base-load heat production, increasing dependency on heat imported from Vantaa. Both cities are expected to employ more cost-competitive biomass boilers to mitigate the reduction in coal-based heat production, which would decrease operational costs but also reduce revenue from electricity sales due to reduced combined heat and power capacity. Consequently, Vantaa is likely to benefit from its substantial storage and waste and biomass combined heat and power capacity, enabling efficient heat production at reduced costs. Across both scenarios, the analysis demonstrates a significant decrease in emissions and less reliance on imported fuels, highlighting the potential benefits of electrified district heating systems even amidst high electricity market prices.