Dispatchable Plants Research Articles

Hydro–Solar Hybrid Plant Operation in a Hydropower Plant Cascade: Optimizing Local and Bulk System Benefits

A hydro–solar hybrid system is an important solution for expanding renewable generation capacity under the percepts of the energy transition. This type of association allows for the coordinated dispatch of solar and hydropower plants, resulting in operational benefits in terms of energy generation and reservoir management, that is, the better use of available water and energy resources. As in this case, the operation of the hydropower plant is associated with the cascade in which it operates, when it is hybridized (for example, by associating with a solar power plant), in addition to local changes, there are impacts on the operating conditions of the other hydropower plants in the same cascade. From such a perspective, this study presents an energy system management model for hybrid power plants composed of hydro and solar sources, aiming to optimize the joint operation and measure the operational consequences at the local level and in the cascade. The results from a case study of a hydro–solar power plant hybridization in the Tietê River (Brazil) revealed increased energy production and improvement in the operating conditions of the cascade’s reservoirs, while the grid capacity was found to be an important constraint that limits the capture of synergies resulting from the generation sources complementarity and thus on the benefits to the cascade.

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.

Resolution of the bi-objective optimization problem for the dispatch of hydroelectric plants under conditions of low inflow using the NSGA II algorithm

Among the consequences of climate change are increased temperatures and changes in rainfall patterns that bring longer periods of drought. This creates limitations in the administration of hydroelectric plant reservoirs, restricting, in some cases, the amount of electrical energy generated. The objective of this research is to solve the multi-objective optimization problem that seeks to minimize the production of electrical energy from hydroelectric plants with low inflow and, at the same time, minimize electrical rationing due to this low production. As these objectives conflict with each other, it is necessary to apply multi-objective optimization problem-solving methodologies, among which are genetic algorithms. The mathematical model is built considering the operating conditions of the reservoirs of the hydroelectric plants under study, including their minimum operating levels, which are included in the model restrictions. The non-dominated genetic classification algorithm II is used to obtain the Pareto front, which is composed of a total of 78 non-dominated solutions that are useful to manage the considered reservoirs at the time of maximum demand. It is recommended to use other multi-objective optimization algorithms for comparison purposes, selecting the ideal indicators to evaluate the performance of each algorithm used, in addition to incorporating monetary and environmental cost restrictions into the model

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%.

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.

Unit commitment and generation dispatch of a hydropower plant in a competitive electricity market

This paper presents a model to solve the short-term scheduling problem of a hydropower plant in a deregulated system, based on dynamic programming techniques. The objective of this model is to maximize the revenue obtained by selling energy in a competitive electricity market. The time horizon of the model is divided into hourly periods and ranges from one day to one week. The proposed model determines both the unit commitment and the power to be generated in each hour of the time horizon. The power is considered as a nonlinear function of the water discharge and the reservoir volume; the dependence of the units and plant operating limits on the available gross head has been taken into account; and the water discharged through the spillway has been also allowed for in the model. This approach has been applied to a practical case study, the results of which are analyzed in detail.

Optimal Short-Term Dispatch of Pumped-Storage Hydropower Plants Including Hydraulic Short Circuit

Optimal Short-Term Dispatch of Pumped-Storage Hydropower Plants Including Hydraulic Short Circuit

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.

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.

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

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

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.

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.

Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty

To solve the problem regarding the large-scale grid-connected consumption of a high proportion of new energy sources, a concentrating solar power (CSP)-photovoltaic (PV)-wind power day-ahead and intraday-coordinated optimal dispatching method considering source load uncertainty is proposed. First, the uncertainty of day-ahead wind power output prediction is described by the multi-scenario stochastic planning method, and the uncertainty of intraday source-load is characterized by the trapezoidal fuzzy number equivalence model. Second, based on the combined scenario set of day-ahead wind power output prediction, the day-ahead optimal dispatch is performed by combining thermal and CSP plants, and the day-ahead thermal and CSP plant dispatch output and intraday source load fuzzy dataset are used as the input quantities for the day-ahead dispatch. Thus, the scheduling output and rotating backup plan for thermal power and CSP plants were determined; finally, the validity and feasibility of the model were verified using arithmetic examples.

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

Flexibility provision by combined heat and power plants – An evaluation of benefits from a plant and system perspective

• Modeling of combined heat and power (CHP) flexibility in an energy system context. • Operational flexibility is important for biomass-fired CHP plant competitiveness. • Net load volatility impacts CHP plant dispatch and use of flexibility measures. • CHP investments are sensitive to fuel cost, in competition with power-to-heat. Variable renewable electricity generation is likely to constitute a large share of future electricity systems. In such electricity systems, the cost and resource efficiency can be improved by employing strategies to manage variations. This work investigates combined heat and power (CHP) plant flexibility as a variation management strategy in an energy system context, considering the operation and cost-competitiveness of CHP plants. An energy system optimization model with detailed representation of CHP plant flexibility is applied, covering the electricity and district heating sectors in one Swedish electricity price area. The results show that investments in CHP plants are dimensioned based on the demand for district heating rather than electricity. In the system studied, this implies that CHP plant capacity is small relative to electricity system variations, and variation management using CHP plants has a weak impact on the total system cost of supplying electricity and district heating. However, flexibility measures increase CHP plant competitiveness in scenarios with low system flexibility (assuming low availability of hydropower or no thermal energy storage) although investments in CHP capacity are sensitive to fuel cost. It is found that while district heating is the dominant CHP product (constituting 50%–90% of the annual CHP energy output), the dispatchable electricity supply has a high value and comprises around 60% of the annual CHP plant revenue. In all scenarios, operational flexibility of the boiler is more valuable than a flexible steam cycle power-to-heat ratio.

Learning with privileged information for short-term photovoltaic power forecasting using stochastic configuration network

The optimal balance and dispatch of power plants in a smart grid require an accurate short-term forecast of photovoltaic (PV) power generation. The climatic condition may have an impact on the PV output, but it is difficult to be used in forecasting due to untimely sampling of meteorological data. To this end, this paper presents an incremental learning using privileged information (LUPI) paradigm for PV power forecasting by using stochastic configuration network. This novel algorithm can employ the meteorological data as privileged information for building PV power forecasting model in the training stage. Additionally, the model performance has been fully discussed in this paper. Finally, experimental results indicate that the proposed model indeed performs favorably in PV power forecasting.

Real‐time active power dispatch of virtual power plant based on distributed model predictive control

With the increasing penetration of renewable energy, virtual power plants (VPP) reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in VPP has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control (DMPC), so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbour information. Simulation results show the feasibility and effectiveness of the proposed method.