Dispatchable Power Plants Research Articles

Simultaneous sizing and energy management of multi-energy Virtual Power Plants operating in regulated energy markets

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.

Dual-time scale optimal dispatch of the CSP-PV hybrid power plant considering dynamic operation

The concentrating solar power (CSP) plant, renowned for its flexibility, environmental friendliness, and energy storage capabilities, has become integral components of large-scale renewable energy projects in China. However, the grid connection methods for CSP plants, wind farms, and photovoltaic (PV) plants are independent. To enhance renewable energy consumption and improve the revenue of CSP and PV plants, this paper proposes a dual-time scale dispatch model for the dynamic operation of the CSP-PV hybrid plant. This model considers the actual startup conditions and dynamic switching models of the subsystems involved in the receiver, thermal storage subsystem, and power cycle. Based on the dynamic operation characteristics of the CSP-PV hybrid power plant, different time scales are used for prediction during day-ahead and intraday periods, improving the efficiency of the hybrid power plant and adapting to weather changes. The dispatch optimization outcomes of the CSP-PV hybrid plant serve as the reported curve for the day-ahead and intraday rolling optimal scheduling process, aiming to minimize the operating costs of the power system. A simulation is conducted to demonstrate the effectiveness of the proposed model, utilizing data from the Dunhuang area, Gansu Province, China.

Scenarios on future electricity storage requirements in the Austrian electricity system with high shares of variable renewables

This paper presents three scenarios (policy, renewables and electrification and efficiency) for transitioning to a 100 % renewable electricity sector in Austria, based predominantly on wind and photovoltaics, alongside sector-specific electrification. Considering renewable expansion targets and three distinctive weather years from an overall system perspective, the core objective is to minimize variable costs of electricity storage and dispatchable power plants. The model developed determines their optimal dispatch for meeting the underlying electricity demand each hour. Within the scenarios for renewable expansion, a special focus lies on integrating short-duration (batteries), medium-duration (pumped storage hydro) and long-duration (hydrogen) energy storage. Our analysis reveals the significant impact of weather patterns on renewable electricity generation, particularly the differences between winter and summer generation quantities. This necessitates seasonal balancing and the mitigation of extremes like low wind power events, which require corresponding backup capacities. This contrast is particularly evident when comparing the years 2030–2050, wherein in the latter, certain dispatchable generators are only utilized in one of the three underlying weather years during extreme weather conditions. In our paper, we demonstrate how, especially for hydrogen production and storage, weather conditions influence production levels and the re-electrification demand. The results indicate the feasibility of achieving a fully decarbonized energy system in Austria through suitable policy measures and expanded renewable generation, with long-duration storage playing a crucial role in seasonal balance and compensating for the absence of fossil fuel generation. Strategic planning is essential to aligning the expansion of renewable energy generation with the necessary flexibility.

Agent based modeling for intraday electricity markets

AbstractIn recent years, the strong growth of renewable energy sources has led to considerable instability in the electricity markets. As a consequence, this has increased trading activities in the continuous intraday market, especially close to delivery. This work presents an agent-based model that is able to reproduce the continuous market evolution, distinguishing players in dispatchable and non-dispatchable power plants and analyzing the behavior and interactions between them. All players behave rationally, trying to maximize their revenues and minimize imbalances. The results show that the model is able to reproduce the main characteristics of the continuous intraday electricity market, such as the price path strongly dependent on internal and external information, such as the wind production forecast, possible outages, an increase in order arrival towards the end of the trading session and weak market efficiency. The strategies assigned to each agent have been formulated taking into account statistical analyses of historical orders placed during continuous trading in different European bidding zones. The analyses have been carried out in a scenario composed of thermal plants with different marginal costs and wind agents, but the flexibility of the model gives the possibility to study many different scenarios.

Dynamic Simulation of a Molten Salt-Steam Superheater for Load-Following Applications

Abstract The growing use of intermittent renewables in electrical grids increasingly motivates load-following operations as a crucial capability of dispatchable power plants. However, frequent load variations in steam generation equipment can cause premature heat exchanger failure. This paper simulates the dynamic behavior of a high pressure, U-tube/U-shell, salt-to-steam superheater typically found in tower-type concentrating solar power subcritical Rankine cycles. Results focus on responses during load-following and inlet temperature changes. The proposed model is a finite volume method, and thermodynamic and heat transfer properties of both fluids are allowed to vary spatially and temporally. Several flow ramping schemes are investigated, including proportionally equal ramps and proportionally dissimilar ramping, where one fluid reaches its mass flow setpoint faster than the other. Results indicate that salt outlet temperature overshoot can occur if ramp rates are of sufficiently high magnitude, and that U-bend metal temperature rate of change can be approximately 2.5× that observed at either outlet. If proportionally-matched ramping is not possible, ramping steam more slowly than the salt is preferred over the alternative, as cold side and U-bend temperature responses are better regulated. Additionally, mass flow rate and inlet temperature changes are shown to elicit unique responses in the tube bundle metal.

Low-Carbon Economic Dispatch of Virtual Power Plants Considering the Combined Operation of Oxygen-Enriched Combustion and Power-to-Ammonia

In order to achieve sustainable development, China has proposed to “strive to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060”. Virtual power plants (VPPs) are an effective means to achieve carbon neutrality goals. In order to improve the economy and low-carbon performance of virtual power plants, this paper proposes a low-carbon economic optimization dispatching model considering the combined operation of oxygen-enriched combustion (OEC) and electricity-to-ammonia (P2A). Firstly, the mechanism of the combined operation of OEC and P2A is proposed. The oxygen-enriched combustion technology can reduce the carbon emissions of the system and enhance the flexibility of the system operation; P2A can effectively consume renewable energy and improve the energy utilization rate. The by-product of the P2A process, oxygen, is the raw material needed for oxygen-enriched combustion, which reflects the complementary nature of the OEC and P2A.Then, an optimal dispatching model is established with the objective function of minimizing the total cost. Finally, the validity of the proposed model is verified by comparing and analyzing the simulation results of five different models. After the introduction of the combined operation of OEC and P2A, the total cost of the system decreases by 10.95%, and the carbon emission decreases by 34.79%.

Potential Revenue from Reserve Market Participation in Wind Power- and Solar Power-Dominated Electricity Grids: The Near-Term, Mid-Term, and Long-Term

As electricity systems become increasingly dominated by variable inverter-based generation (such as wind and solar photovoltaics (PV)), additional sources of variability appear, while the share of dispatchable thermal power plants decreases. Maintaining a stable grid frequency requires new sources of inertia to slow the frequency changes, as well as new sources of reserve power to counter the imbalances. This work uses a linear optimization modeling approach to analyze the reserve market of the electricity system during the transition to a carbon-free system with high shares of variable electricity generation. The modeling is performed for three regional contexts and with varying degrees of available flexibility technologies. As for the reserve supply, batteries, electrolyzers, electric boilers and heat pumps for district heating, curtailed wind and solar power, and hydropower and thermal power plants are all included. The results indicate that of these suppliers of reserves, the introduction of grid-scale batteries into the system drastically reduces the reserve market size. Only early in the transition is revenue from the reserve market greater than 5% of the total revenue for any technology. While the demand for reserves increases as the share of solar PV and wind power increases, the modeling reveals that access to flexible loads, storage units, and emulated inertia from wind power also increases. Depending on the choice of flexibility measures available, demand-side participation could play a major role in minimizing the cost of grid stability in the future.

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.

Low-Carbon Economic Dispatch of Virtual Power Plant Considering Hydrogen Energy Storage and Tiered Carbon Trading in Multiple Scenarios

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Day-ahead optimal dispatch of a virtual power plant in the joint energy-reserve-carbon market

As an efficient technique to manage massive distributed energy resources (DER), the virtual power plant (VPP) can leverage the aggregated flexibility to enhance the economy and security of the system. However, few efforts have been reported on the carbon emission reduction benefit of the VPP. In this paper, a novel incentive mechanism for low-carbon demand response is designed, which is based on dynamic pricing and carbon intensity. Considering the uncertainty of renewable energy output characterized by the chance-constrained approach, a coordinated optimization model in the joint day-ahead energy, reserve and carbon market for the VPP operator and flexible consumers is proposed, including the pre-dispatch model, the energy storage systems (ESSs) incorporated carbon emission flow calculation model, and the bi-level final-dispatch model. Numerical simulations of both small-scale and large-scale cases demonstrate that the proposed low-carbon dispatch strategy can boost the payoff of the VPP operator, reduce carbon emissions in the joint energy-reserve-carbon market, and improve the robustness of the power system against uncertainty.

Making solar electricity dispatchable: A technical and economic assessment of the main conversion and storage technologies

The techno-economic performances of five different solar-electricity conversion technologies (photovoltaic, solar tower, parabolic trough as well as two hybrid PV/CSP systems) associated with three energy storage means (electrochemical, thermal, and thermophotovoltaic) are evaluated thanks to representative models applied to four representative sites around the world. The evaluation is based on the ability to dispatch the power production throughout the year, the ability to maximize energy injection in the electrical grid, and the levelized cost of electricity. It is found that increasing the dispatchability of solar power plants will necessarily lead to the emergence of additional energy losses and important LCOE increase, either because of low round-trip efficiency of the storage system, or because of its high cost of energy capacity. Despite lower energy production for a given collecting area, combination of PV power plants with electrochemical storage or thermal energy storage surprisingly seem to be the most promising paths.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi-objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and demand response. Therefore, the work contributes to decreasing the economic and environmental costs of multi-energy complementary virtual power plant and better enhancing the consumption ratio of renewable energy.

Low-carbon economic dispatch of Photovoltaic-Carbon capture power plant considering deep peak regulation

Retrofitting Coal-fired Power Plants (CFPPs) with carbon capture equipment not only reduce carbon emissions but also provide a deeper peaking depth to accommodate renewable energy. Nevertheless, there exist no studies addressing the economics of deep peaking of Carbon Capture Power Plants (CCPPs), and few studies concerns the synergistic dispatch of PV-CCPP. Therefore, this study analyzes the deep peaking characteristics of CCPP and proposes a low-carbon economic dispatch model for PV-CCPP. Case study shows that the typical daily peaking depth of CCPP is reduced by 72%, the carbon emission reduction is 4263t, and the annual system economy is improved by 43%. Taking into account the peaking compensation helps to improve the system economy, but the carbon emission reduction is slightly reduced.

Economic scheduling of virtual power plant in day-ahead and real-time markets considering uncertainties in electrical parameters

The virtual power plant (VPP) concept, which offers energy efficiency, benefits significantly from the tremendous potential of integrating distributed energy resources (DERs). This work proposes optimal economic dispatch of virtual power plant (VPP), and the problem is formulated in a novel way considering the potential of energy storage systems (ESS) of electric vehicles (EVs) and data center (DC) in a data center facility test model using optimized energy management system (EMS). The problem is evaluated for maximizing the revenue for VPP considering market prices and the uncertainties in the distributed energy resources. To this purpose, a techno-economic analysis of the proposed test system of VPP is evaluated using mixed-integer linear programming (MILP). Renewable generation, electricity price, load demand, and real-time EV behavior uncertainty analysis are forecasted using Long Short-Term Memory (LSTM) along with DC battery degradation analysis which constitutes the optimization model. The results showed that the VPP EMS can perform optimal power scheduling with the day ahead and real-time electricity price scenarios. Moreover, further analysis including battery degradation cost of DC UPS ESS proved the capability of the proposed framework by up to 2064.4 $/h revenue for day-ahead price and 562 $/h revenue for the real-time price compared to revenue without battery degradation cost. A simulation scenario is also carried out where the constant load demand and the battery storage powers are varied between 50% and 150%. It can be concluded that the 500 kW and 750 kW ESS (100% and 150%) are better choices as compared to 250 kW (50%) during increased load demand for investors due to their higher revenue. Hence it is concluded that this research highlights the importance of revenue benefits from deploying VPP with the collaborative operation of DERs.

Understanding electricity prices beyond the merit order principle using explainable AI

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Various market models are based on this principle when attempting to predict electricity prices, yet the principle is fraught with assumptions and simplifications and thus is limited in accurately predicting prices. In this article, we present an explainable machine learning model for the electricity prices on the German day-ahead market which foregoes of the aforementioned assumptions of the merit order principle. Our model is designed for an ex-post analysis of prices and builds on various external features. Using SHapley Additive exPlanation (SHAP) values we disentangle the role of the different features and quantify their importance from empiric data, and therein circumvent the limitations inherent to the merit order principle. We show that load, wind and solar generation are the central external features driving prices, as expected, wherein wind generation affects prices more than solar generation. Similarly, fuel prices also highly affect prices, and do so in a nontrivial manner. Moreover, large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite plants. Overall, we offer a model that describes the influence of the main drivers of electricity prices in Germany, taking us a step beyond the limited merit order principle in explaining the drivers of electricity prices and their relation to each other.

Low-carbon optimal dispatch of virtual power plant based on time-of-use ladder carbon emission rights exchange mechanism

In view of the problems of abandoning wind and solar energy, and carbon emissions, inevitable reduce of thermal power generation caused by the consumption of wind and solar energy, and the changes in the proportion of wind and solar energy consumption to thermal power at different times under the influence of time-of-use price, a time-of-use ladder carbon emission rights exchange mechanism is proposed, and on this basis, a low-carbon virtual power plant optimal dispatching model with ‘wind-solar-gas-storage’ is built. The maximum daily operating income of the virtual power plant is taken as the objective function in the model. The IBM commercial solver CPLEX is employed to optimize the solution, so as to obtain the optimal economic dispatching strategy of the virtual power plant, and compares it with the set scenarios. The simulation results reveal that the proposed mechanism can effectively enhance the enthusiasm and operating benefits of virtual power plants for renewable energy consumption, and reduce carbon emissions, providing new methods for low-carbon dispatching, and wind and solar energy consumption of virtual power plants.

Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode

To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

Risk-Aware Active Power Flexibility Allocation From TSO–DSO Interconnections: Switzerland's Transmission Network

The power flexibility of the proliferating distributed energy resources located in distribution networks could be aggregated and provided to the transmission system. Considering the lack of a framework to help transmission system operator (TSO) to benefit from these potential power flexibility resources, first, the article establishes a framework for modeling <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ggregated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> l <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> xibilities of distribution <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</u> etworks (AFENs) as seen from TSO's perspective. Then, it develops a two-stage linear stochastic optimization model to optimally book the TSO's required size of active power flexibility from AFENs and dispatchable power plants (DPPs). The model leverages a cost-benefit method and a dc load-flow model to minimize the TSO's total cost, namely the sum of 1-expected cost of active power flexibility allocation from AFENs and DPPs and 2-expected cost of energy not supplied. To achieve a risk-aware economic balance between these two incurred costs of TSO, the model relies on the value of lost load index as a metric. The method considers credible contingencies along with forecast errors of renewable generation and loads as scenarios. Finally, the method is applied to the transmission grid of Switzerland, operated by Swissgrid, to illustrate its effectiveness.

Simultaneous design and operational optimization of hybrid CSP-PV plants

The dispatchability of renewable power plants and the role of energy storage are gaining relevance leading to the development of hybrid CSP-PV plants. This work investigates the optimal design of highly integrated CSP-PV power plants. The integration occurs not only at grid level (synergic operation as a virtual power plant), but also through the introduction of electric heaters, placed in parallel to the solar field: the excess PV electricity is converted into heat by electric heaters and stored in the CSP hot storage tank. The whole system design is optimized devising an ad hoc Mixed Integer Linear Program (MILP) which co-optimizes design and operational variables with accurate linearization of all nonlinear effects. Compared to the respective stand-alone technologies (only CSP or PV plus battery), the results show that the hybrid solutions can achieve similar or better dispatchability levels at a lower cost of electricity (LCOE reduction between −30% and −50%).