Combined Power Plant Research Articles

Opportunity Assessment of Virtual Power Plant Implementation for Sustainable Renewable Energy Development in Indonesia Power System Network

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.

Thermodynamic Analysis and Performance Improvement in Biomass Power Plant: A Comprehensive Review

Biomass power plants play a crucial role in the sustainable energy sector by converting organic materials such as agricultural residues, forest biomass, and dedicated energy crops into electricity and heat. Biomass power plants represent a viable and renewable energy solution that can contribute to transitioning to a low-carbon economy. Their ability to convert organic materials into electricity and heat, coupled with proper management of feedstocks and emissions, can provide a sustainable alternative to fossil fuel-based power generation. Biomass has appeared as one of the most encouraging renewable energy sources for the replacement of fossil fuels. An extensive study about the prospective of biomass to produce renewable energy in the world has been exhibited in this article. The biomass-driven combined heat and power plant demonstrates 67% and 12% efficiency improvement compared to the stand-alone biomass power plant. BFP-CCS performs best at the H<sub>2</sub>O/Mn<sub>2</sub>O<sub>3</sub> mass ratio of 1.6, the H<sub>2</sub>O/O<sub>2</sub> molar ratio of 2.8, the O<sub>2</sub>/biomass mass ratio of 0.22, and the fuel utilization factor of 0.65. The exergo-economic and exergo-environmental factors obtained are 51.5% and 0.0288% respectively at the favorable operating conditions. The round-trip efficiency of the process using R1233zd was 8.77%, which was slightly lower than that of the process using R245fa (8.84%). The net power output of the final CLC integrated configuration and conventional power plants are 492.19 kW and 273.12 kW respectively. A lot of energy can be recovered without low-temperature corrosion problems, and 7% of the total input energy can be saved. The maximum obtainable exergy efficiency was 42.03%, which was related to MSW (Municipal Solid Waste). The primary aim of this review is to furnish a thorough understanding of the thermodynamic complexities and potential improvements within biomass power plants. Through a critical analysis of current research and emerging technologies, this review aims to establish the groundwork for more effective and sustainable energy production from biomass, playing a crucial role in shaping a cleaner and more environmentally friendly future.

Grid Integration for Electric Vehicles: A Realistic Strategy for Environmentally Friendly Mobility and Renewable Power

The promotion of electric vehicles (EVs) as sustainable energy sources for transportation is advocated due to global considerations such as energy consumption and environmental challenges. The recent incorporation of renewable energy sources into virtual power plants has greatly enhanced the influence of electric vehicles in the transportation industry. Vehicle grid integration offers a practical and economical method to improve energy sustainability, addressing the requirements of consumers on the user side. The effective utilisation of electric vehicles in stationary applications is highlighted by technological breakthroughs in the energy sector. The continuous advancement in science and industry is confirming the growing efficiency of electric vehicles (EVs) as virtual power plants. Nonetheless, a thorough inquiry is imperative to elucidate the principles, integration, and advancement of virtual power plants in conjunction with electric automobiles, specifically targeting academics and researchers in this field. The examination specifically emphasises the energy generation and storage components used in electric vehicles. In addition, it explores several vehicle–grid integration (VGI) configurations, such as single-stage, two-stage, and hybrid-multi-stage systems. This study also considers the various types of grid connections and the factors related to them. This detailed investigation seeks to offer insights into the various facets of incorporating electric vehicles into virtual power plants. It takes into account technology improvements, energy sustainability, and the practical ramifications for users.

Unlock the aggregated flexibility of electricity-hydrogen integrated virtual power plant for peak-regulation

This paper proposes an aggregated flexibility estimation method considering the distributed electricity-hydrogen (H2) interactions for virtual power plants (VPPs) to enhance the economic benefits from the peak-regulation market (PRM) while facilitating the accommodation of renewable generation. Firstly, various distributed energy resources (DERs) such as electric vehicles (EVs), air conditioning systems (ACs), and electricity-H2 coupled hydrogen refueling stations (HRSs) are modeled as virtual storage (VS) with characterized charging/discharging response parameters. Besides, the temporal coupling power profile of VS can be abstracted as a polytope from the geometric perspective, and the aggregated high-dimensional flexibility region of VPP is formulated by the Minkowski sum for these polytopes. Furthermore, a day-ahead peak-regulation optimal scheduling model considering the conditional value at risk (CVaR) is developed for the electricity-H2 integrated VPP participating the PRM, and the dimensionality of the aggregated polytope is reduced by the inscribed hyperbox approximation to provide a compact and concise scheduling region. Comparative studies have validated the aggregated power range and net income can be improved by 13.05% and 15.11% with the mechanism of electricity and hydrogen integrations.

Optimal economic dispatch of a virtual power plant based on gated recurrent unit proximal policy optimization

The intermittent renewable energy in a virtual power plant (VPP) brings generation uncertainties, which prevents the VPP from providing a reliable and user-friendly power supply. To address this issue, this paper proposes a gated recurrent unit proximal policy optimization (GRUPPO)-based optimal VPP economic dispatch method. First, electrical generation, storage, and consumption are established to form a VPP framework by considering the accessibility of VPP state information. The optimal VPP economic dispatch can then be expressed as a partially observable Markov decision process (POMDP) problem. A novel deep reinforcement learning method called GRUPPO is further developed based on VPP time series characteristics. Finally, case studies are conducted over a 24-h period based on the actual historical data. The test results illustrate that the proposed economic dispatch can achieve a maximum operation cost reduction of 6.5% and effectively smooth the supply–demand uncertainties.

Control strategy and research on energy storage unit participation in power system frequency regulation based on VSG technology

When large-scale photovoltaic power plants are integrated into the grid, it will cause the grid-connected capacity to gradually expand, causing greater harm to the safe and stable operation of the power system, which will affect the FM characteristics of the power system. This requires the PV power plant to actively participate in power system frequency control. Through the PV virtual synchronous generator frequency control technology, coupled with the virtual synchronous PV power plant modeling, the PV new energy units can have the same frequency control characteristics as synchronous generator sets. In the virtual synchronous technology, due to the existence of damping windings, electromagnetic damping will be generated, and it is also due to virtual damping that the virtual synchronous technology has the ability to damp power oscillations. It is also because of the excellent virtual inertia and damping characteristics of the virtual synchronous generator in the grid that this FM technology helps to regulate the frequency fluctuations of the new energy grid, meet the requirements of the power system to maintain stable operation, and then realize the large-scale PV grid connection.

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The 4E emergy-based analysis of a novel multi-generation geothermal cycle using LNG cold energy recovery

Over the last decade, the increasing demand for sustainable energy sources has led to the development of innovative approaches in geothermal power plant cycles. In this study, a novel combined geothermal power plant cycle (GTPPC) that integrates the Kalina cycle, transcritical Carbon Dioxide (T-CO2) cycle, with the injections of the Liquid Natural Gas (LNG) stream and geothermal water (GTW) stream is proposed in order to provide cold and hot sources of the system. The performance of the system is evaluated using emergy analysis, an evaluation factor based on thermodynamic principle and solar energy with the aid of emergoeconomic and emergoenvironmental factors. The injection of LNG stream leads to significant improvements: enhancing the net output power and overall emergoeconomic and emergoenvironmental efficiencies of the cycle. By conducting improvement priority and parametric studies, crucial stream points and the system's performance's optimization is identified. Our results indicate that the Kalina sub-cycle has a greater impact on performance, and the exergy destruction in the LNG stream will be minimized. At the optimal point, the GTPPC achieves high thermal efficiency and emergy-based metrics, indicating its sustainability and viability as a geothermal power generation solution. By the injection of LNG stream, the net output power, and overall emergoeconomic and emergoenvironmental efficiencies of the cycle, improved by 1.79 MW, 14 %, and 16 %, respectively. According to the obtained results, the performance of the Kalina sub-cycle has more effect than the Carbon Dioxide sub-cycle, and destructing exergy in the LNG stream will have irreparable effects on the operation of the GTPPC. At the optimum point of the system performance, the GTPPC will operate with the thermal efficiency, ψ, and φ of 15.4 %, 94.3 %, and 98.5 %, respectively.

A profitability optimization approach of virtual power plants comprised of residential and industrial microgrids for demand-side ancillary services

Nowadays, the high proportion of renewable energy integration continuously boosts the grid’s demand for regulation capacity. But the reasonable utilization remains challenged between the regulation capacity of demand-side resources and the complementary characteristics between market players. This paper primarily investigates the issues of complementary synergy among different types of resources and different microgrids. This is currently a focal point in enhancing the regulation capacity of the power grid. This study considers a variety of resources to form microgrids with distinct characteristics, including residential and industrial loads as typical demand-side resources, which have distinct power and energy features in the demand-side regulation market. Furthermore, this study considers virtual power plants (VPPs) consisting of multiple microgrids (MMGs) in the energy market and regulation market as a two-stage problem with day-ahead scheduling and real-time energy consumption control. Based on this, we develop optimal scheduling models and an adaptive MG participation mechanism to maximize the economic benefit of the VPP and help ensure the frequency stability of the power grid. Innovatively, this work designs an adaptive MG participation mechanism, when the operational cost of the adaptive microgrid is high and the regulation capacity of other microgrids is sufficient to provide frequency support for the grid, the adaptive microgrid will be set as backup. Case studies are conducted to verify the proposed profitability optimization method for VPP.

Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.

Retrofitting Biomass Combined Heat and Power Plant for Biofuel Production—A Detailed Techno-Economic Analysis

Existing combined heat and power plants usually operate on part-load conditions during low heating demand seasons. Similarly, there are boilers designated for winter use that remain inactive for much of the year. This brings a concern about the inefficiency of resource utilization. Retrofitting existing CHP plants (especially for those with spare boilers) for biofuel production could increase revenue and enhance resource efficiency. This study introduces a novel approach that combines biomass gasification and pyrolysis in a polygeneration process that is based on utilizing existing CHP facilities to produce biomethane, bio-oil, and hydrogen. In this work, a detailed analysis was undertaken of retrofitting an existing biomass combined heat and power plant for biofuel production. The biofuel production plant is designed to explore the polygeneration of hydrogen, biomethane, and bio-oil via the integration of gasification, pyrolysis, and renewable-powered electrolysis. An Aspen Plus model of the proposed biofuel production plant is established followed by a performance investigation of the biofuel production plant under various design conditions. An economic analysis is carried out to examine the profitability of the proposed polygeneration system. Results show that the proposed polygeneration system can achieve 40% carbon efficiency with a payback period of 9 years and an internal rate of return of 17.5%, without the integration of renewable hydrogen. When integrated with renewable-power electrolysis, the carbon efficiency could be significantly improved to approximately 90%; however, the high investment cost associated with the electrolyzer system makes this integration economically unfavorable.

Multi-objective two-stage optimization scheduling algorithm for virtual power plants considering low carbon

Abstract With the continuous growth of energy demand, the importance of developing and utilizing low-carbon energy is becoming increasingly prominent. In this context, virtual power plant technology has been proposed. It is a technology for integrating, coordinating and optimizing distributed energy resources, which has significant effects in improving energy utilization efficiency and reducing carbon emissions. This article proposed a virtual power plant scheduling method based on multi-objective two-stage optimization scheduling algorithm considering low carbon. This method first determines the production and consumption of various energy resources in the virtual power plant, including wind power, thermal power and hydropower. Then, with the goal of minimizing costs and reducing carbon emissions, multi-objective optimization algorithms are used to allocate and schedule energy resources in the virtual power plant. On this basis, a two-stage optimization strategy was introduced, combining long-term optimization and short-term scheduling to adapt to energy allocation and scheduling needs at different time scales. The experimental results indicate that this method can effectively improve the energy utilization efficiency and economy of virtual power plants, and reduce carbon emissions.

Research on decentralized resource operation optimization of virtual power plant with 5G base station

Abstract The extensive construction and promotion of 5G base stations (5GBSs) have led to a surge in communication energy consumption, as 5G energy consumption is about three to five times that of 4G. To reduce the energy consumption of 5GBS, this article incorporates 5GBS into power demand side management and proposes a flexible resource collaborative optimization method that integrates 5GBS with virtual power plants (VPPs). Firstly, starting from the basic structure of 5GBS, the adjustable potential of 5GBS and the paths for participating in VPP optimization were analyzed. Secondly, with the minimum operating cost as the optimization objective and considering the constraints of 5GBS and various decentralized resources, a demand side decentralized resource collaborative optimization model for VPP integrating 5GBS was established. This model can be solved using the Non-dominated sorting genetic algorithm II (NSGA-II) algorithm. The results indicate that the participation of 5GBS in power demand side management can reduce their comprehensive energy consumption and operating costs.

Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations

Amidst high penetration of renewable energy, virtual power plant (VPP) technology emerges as a viable solution to bolster power system controllability. This paper integrates a novel flexible load, 5G base stations (gNBs) with their backup energy storage systems (BESSs), into a VPP for power system real-time economic dispatch (RTED). Leveraging BESSs dispatchable capacity, the VPP offers power support and gains economic incentives, where the dispatchable capacity is estimated based on distribution network reliability and gNB availability requirements. Then, an efficient and refined VPP management strategy is proposed, where numerous gNBs are aggregated into limited number of virtual generators (VGs). The VPP dispatchable capacity can be efficiently and accurately evaluated via VGs states. Each VG is assigned a control cost function, enabling optimal control decisions resembling power system RTED, to minimize overall VPP control costs. Finally, a power support and incentive determination mechanism is presented based on the Leontief union bargaining model, ensuring reciprocal transactions between VPP and power system. To solve the Leontief model and address information privacy issues between the two parties, a distributed iterative algorithm is developed. Simulations based on actual data and IEEE 118-bus system illustrate that the proposed approach can smoothen intra-day load profiles, achieving mutual economic benefits for the power system and VPP without compromising the security of 5G network operations.

Research on the collaborative management of internal and external fluctuations and optimization of power trading in multi-virtual power plants

Virtual power plants (VPPs), serving as an integration and coordination platform for energy sources, have been rapidly developed in recent years. With the rapid expansion of distributed energy sources, disturbance problems within the VPP and cluster are becoming increasingly prominent. In this study, we commence by addressing the internal fluctuations within the VPP through the construction of a source–load uncertainty model. Then, we integrate the Nash bargaining game theory, treating different VPPs as participants in the game. This approach significantly mitigates disturbances within both VPPs and the cluster through the negotiation of power trading strategies. In addition, the coordination between VPPs and their coordination with the distribution network in the network-wide interaction is considered, and an optimization algorithm for distributed electricity trading based on the alternating direction method of multipliers is proposed to solve the model. The results show that the proposed model effectively copes with the internal and external disturbances of the VPP, improves the system’s ability to cope with the uncertainty risk, and reduces the operation cost.

Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach

A virtual power plant (VPP) is typically a collection of distributed energy resources (DERs) aggregated by an energy service provider (ESP). However, recruiting DER owners to participate in a VPP is challenging. Therefore, we propose a profitable and flexible VPP recruitment-participation approach that incorporates both long-term regular recruitment and short-term casual recruitment. Casual recruitment caters to ambitious DER participants, consisting of fair and bet-on modes. The latter establishes a set of pre-determined payoff conditions, the fulfillment or non-fulfillment of which confers the participants a contractual right to get compensation from the ESP. To ensure the success of the proposed recruitment approach, we address two key problems. First, we introduce a new index, unconventional arbitrage opportunity (UAO), for evaluating future profits and propose a conditional time series generative adversarial network to predict UAO with weather conditions. Second, we introduce a payoff allocation method that combines fairness and incentives to motivate casual DER participants. The incentive coefficients are optimized using an improved deep reinforcement learning algorithm. Case studies are conducted to verify the proposed recruitment-participation approach, the effectiveness of the UAO prediction model, and the optimized incentive coefficients.

Application of New Filling Material Based on Combined Heat and Power Waste for Sewage Treatment in Constructed Wetlands.

The filling of constructed wetlands (CWs) affects the efficiency of sewage treatment and proper operation. Mineral aggregates are most often used as filling materials. Significant environmental burdens from mineral mining operations justify the search for waste fill. This study aimed to determine the possibility of increasing the efficiency of CW by using a Certyd aggregate as a new filling. Certyd is produced in the sintering process of coal ash, a waste from combined heat and power (CHP) plant operation. Comprehensive two-year studies were conducted using two real-scale subsurface vertical flow (SS VF) CWs supplied with domestic sewage. One bed was filled with a Certyd and the other was filled with appropriate fractions of a mineral aggregate. Both beds worked in parallel, and to compare their effectiveness, account seasonality was taken into account. The SS-VF Certyd-filled bed achieved an average efficiency of 88.0% for biological oxygen demand (BOD5), 80.2% for chemical oxygen demand (COD), 80.4% for suspended solids (SSs), 80.2 for ammonia nitrogen (N-NH4), 72.2% for total nitrogen (TN), and 55.3% for total phosphorus (TP), while the gravel-filled bed achieved 84.5%, 77.0%, 86.9%, 74.2%, 69.4%, and 57.8% for the whole research period, respectively. A higher effect of the removed unit load was achieved in the bed filled with Certyd (36.2 g BOD5 m-2 d-1, 50.0 g COD m-2 d-1, 5.88 g SS m-2 d-1, 7.1 g TN m-2 d-1, 7.9 g N-NH4 m-2 d-1, 0.79 g TP m-2 d-1) compared to the gravel-filled bed (34.7 g BOD5 m-2 d-1, 47.0 g COD, 6.35 g SS m-2 d-1, 6.9 g TN m-2 d-1, 7.3 g m-2 d-1 N-NH4, 0.83 g TP m-2 d-1).

Applicability of multi-agent control for virtual inertia modes in a wind power plant

This work presents a literature review devoted to increasing the efficiency and quality of managing wind power plants. The analysis focuses on mitigating the adverse effects of wind turbines on the power system and providing system services, such as primary frequency regulation. Nearly 150 scientific publications and reviews, selected from various scientific sources (such as IEEE, Web of Science and Scopus) by the keywords, including “wind power station”, “wind turbine”, “multi-agent control”, “virtual inertia”, “microgrid”, “virtual power plant”, and “frequency control”, were evaluated.A systematic review methodology of specialised sources was applied, which offers a defined structure for this field of research by categorising articles. The study emphasises the urgency of developing technologies to increase the regulation capacity of a wind power plant, since the low inertia of renewable energy sources leads to a decrease in the stability of power systems, a significant proportion of which accounts for wind power plants. It follows from the literature review that one of the means to increase the stability of such power systems is the creation of virtual inertia for wind power plants. However, due to the limited capacity and control capabilities of each individual wind turbine, the efficiency of introduced virtual inertia may be insufficient, when implemented for individual units. Moreover, it is shown that uncoordinated control can affect the stability of the system. In this review, the specific sources considering coordinated multi-agent control of the virtual inertia for several wind turbines (wind power plants) were analysed. The review concludes that the proposed approach is currently understudied, while the outlined theses can be confirmed by developing the necessary algorithms and analysing the results.

Transformation to a renewable electricity system in Austria: Insights from an integrated model analysis

We analyse the (techno- and macro-)economic and distributive effects of a transformation to a renewable electricity system in Austria by 2030, as stipulated by the Austrian government. For the analysis, the macroeconomic model DYNK and ATLANTIS, a partial model of the electricity market, were expanded and linked. Four transformation scenarios conforming with the 100% renewable electricity target in Austria on a national balance are examined, integrated into a consistent scenario for the development of the European electricity system. Additionally, sensitivity analyses with respect to the gas price are performed. Although all scenarios achieve 100% renewable electricity on a national balance, the analysis shows that electricity from gas-fired power plants will still be needed in 2030 to balance variable renewable generation, to avoid grid congestion, and for heat generation from combined heat and power plants in winter months. Another main conclusion from the simulations is that the transition towards a renewable electricity sector is almost neutral from a socio-economic perspective. It does neither reveal harmful impacts nor lead to high multiplier effects from additional investment. With high natural gas prices in the sensitivity scenarios a decrease in GDP and household income, which might motivate redistributive policies, can be observed.

A multi-objective dispatching model for a novel virtual power plant considering combined heat and power units, carbon recycling utilization, and flexible load response

To optimize the energy supply potential and complementary advantages of distributed energy, this paper focuses on the dispatching optimization of cogeneration virtual power plant considering uncertainty. First of all, wind power, photovoltaic, combined heat and power (CHP) units, electric boilers, and controllable loads are integrated into a CHP virtual power plant. Then, carbon capture and electric-to-gas devices are introduced to realize carbon recycling. Furthermore, quantify the risk of real-time dispatching of virtual power plants through uncertainty scenario generation and conditional value at risk (CVaR) theory, and the multi-objective stochastic dispatching optimization model of virtual power plants is built with the aim at minimizing the operation cost, carbon emissions, and operation risk as the objectives, and the CRITIC weighting method is adopted to solve it. Finally, the calculation results show that: 1) the electric boiler can use wind and photovoltaic power to supply heat for the system, reduce the dependence of the virtual power plant (VPP) on the CHP unit, and make the electric output of the unit more flexible. 2) The risk quantification method proposed can fully measure the risk situation in real-time dispatching, arrange the wind and photovoltaic power generation plan and backup plan more reasonably, and enable the VPP to get more benefits while avoiding the risks in real-time dispatching.