Dispatchable Power Plants Research Articles

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.

Thermal Energy Storage (TES) Prototype Based on Geopolymer Concrete for High-Temperature Applications.

Thermal energy storage (TES) systems are dependent on materials capable of operating at elevated temperatures for their performance and for prevailing as an integral part of industries. High-temperature TES assists in increasing the dispatchability of present power plants as well as increasing the efficiency in heat industry applications. Ordinary Portland cement (OPC)-based concretes are widely used as a sensible TES material in different applications. However, their performance is limited to operation temperatures below 400 °C due to the thermal degradation processes in its structure. In the present work, the performance and heat storage capacity of geopolymer-based concrete (GEO) have been studied experimentally and a comparison was carried out with OPC-based materials. Two thermal scenarios were examined, and results indicate that GEO withstand high running temperatures, higher than 500 °C, revealing higher thermal storage capacity than OPC-based materials. The high thermal energy storage, along with the high thermal diffusion coefficient at high temperatures, makes GEO a potential material that has good competitive properties compared with OPC-based TES. Experiments show the ability of geopolymer-based concrete for thermal energy storage applications, especially in industries that require feasible material for operation at high temperatures.

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.

Bi-level Optimizations of Multi-Components Virtual Power Plant for Economic Dispatch and Renewable Utilization

The significant injections of renewable power have been leading to risks of safety and stability of power system by curtailing system inertia. Based on the requirement of ‘carbon neutralization’ policy, independent participants including energy storage (ES), load demand response (LDR) and virtual power plant (VPP) are introduced into system as innovative ancillary service methods. This paper proposes a bi-level optimization method to achieve the economic dispatch of multi-components virtual power plant consisting with gas generator, PV, wind turbine, energy storage and electricity vehicle in the day-ahead optimization, while achieving the maximum renewable power utilization in the real-time optimization by adjusting dispatch plan. This method considers constraints including solar/wind power forecasting, load level forecasting, ES charging/discharging constraints, and DC power flow limits. A case study based on practical project is present, showing that the bi-level optimization is capable of achieving diversified resources aggregation in extended region, as well as the economic optimization and green energy consumption target.

CFD and FEM analysis to migrate from fixed- to variable-speed on the Alto Lindoso HPP

The need to decarbonise our energy system push us toward the use of different clean renewable energy sources, such as solar, wind and hydroelectric energy. It is well known that some of these renewable energy sources suffer of intermittence, which requires the conventional dispatchable power plants to become more flexible to respond to any power change to ensure a stable electrical grid. For this specific purpose, the European project XFLEX HYDRO is focusing on the hydropower sector, with seven demonstrators in three different countries to demonstrate the possibility to enhance the flexibility, one option being to use the variable-speed technology combined with smart digital control. Among these seven demonstrators, one is the Alto Lindoso hydropower plant owned by EDP and located in North-West of Portugal. It consists of two fixed-speed vertical Francis turbines with a high head of approximately 275 m and a rated power of 317 MW. The objective of this demonstrator is to assess the potential of a variable speed using the Doubly Fed Induction Machine (DFIM) technology to increase the flexibility of the power and the lifetime of the turbine. Therefore, this paper explores numerically the hydraulic behaviour of the model runner when operated at a selected part load conditions (60% BEP) with the rotational speed changed by ±10%. Second, a FEM analysis is carried out to evaluate the runner blades damage rate. This preliminary study showed that in term of wear and tear, the variable speed technology would bring in an advantage.

Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

AbstractThe volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short‐term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt‐ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE‐SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional‐link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE‐SCN performs favorably in the terms of the prediction accuracy.This article is categorized under: Technologies > Prediction

What Incentives Are Required for Renewables to Provide Redispatch Services? A Simulation in a Multi-Market Setting

Renewable energy sources (RES) can provide valuable flexibility potential for multiple markets and grid services in the future. In this paper, the focus lies on the development of algorithms for an optimal dispatch and bidding of a RES-based virtual power plant (VPP) by considering current short-term and balancing market conditions in Austria, as well as a proposed redispatch market. Specifically, different pricing and bidding strategies for a redispatch market are compared and their feasibility is analyzed. The attractiveness of different pricing models and remuneration mechanisms for redispatch, as well as the influence of the redispatch call probability on the bidding behavior of the VPP, is investigated. The simulation of the bidding behavior is carried out using linear optimization techniques. The paper describes the algorithms as well as the assumptions for market rules in redispatch, balancing and short-term electricity markets. The results show that the probability to be activated for redispatch and thus the required incentives depend highly on the location of the plant. Pre-curtailment of the plant to offer positive redispatch is not recommended as the prices would need to be set at up to 200–300% of the day-ahead price.

Multiobjective Load Dispatch for Coal-Fired Power Plants under Renewable-Energy Accommodation Based on a Nondominated-Sorting Grey Wolf Optimizer Algorithm

Coal-fired power plants are widely used to achieve a power balance in grids with renewable energy, which leads to new requirements for speediness in load dispatch. This paper presents a nondominated-sorting grey wolf optimizer algorithm (NSGWO) for the multiobjective load dispatch of coal-fired power plants that employed efficient nondominated sorting, a reference-point selection strategy, and a simulated binary crossover operator. The optimization results of the benchmark functions indicated that the NSGWO algorithm had a better accuracy and a better distribution than the traditional multiobjective grey wolf optimizer algorithm. Regarding the load dispatch of economy, environmental protection, and speediness strategies, the NSGWO had the best performance of all the simulated algorithms. The optimal-compromise solutions of the economy and speediness strategies of the NSGWO algorithm had a good distribution, which elucidated that this novel algorithm was favorable to allowing coal-fired power plants to accommodate renewable energy.

Chaotic Butterfly Optimization Algorithm Applied to Multi-objective Economic and Emission Dispatch in Modern Power System

Aims : To optimize the economic and emission dispatch of the thermal power plant. Background: Considering both the economic and environmental aspects, a combined approach had made to attain a solution is known as the combined economic and emission dispatch problem. The CEED problem is a nonlinear bi-objective problem with conflicting behaviour with all the practical constraints. Objective: A new optimization method is improvised by applying the chaotic mapping to the butterfly optimization algorithm. This method is applied to the Combined Economic and Emission Dispatch (CEED) problem for optimizing consumed fuel cost and produced environment pollutant. Methods: Improved Chaotic Butterfly algorithm is applied to the optimization problem to optimize combined economic and emission dispatch. Result : The proposed technique is tested for four different test systems with various practical constraints like valve point loading, ramp rate limit and prohibited operating zones. The obtained results from the chaotic butterfly optimization algorithm (CBOA) are compared with other optimization techniques provide an optimum solution for CEED problem. Conclusion : Considering the environmental impact the novel metaheuristic swarm intelligence technique is applied with conflict of interest. Different test systems, with different practical operational constraints like valve-point loading, prohibited operating zones and ramp rate limits and emission dispatch have been analyzed to validate the implementation of the proposed algorithm in real life CEED problem situations.

Optimal dispatch of a thermal-electric virtual power plant considering heat storage tanks

Characteristic of thermoelectric coupling of Combined heat and power (CHP) units reduces the flexibility of power generation and heat generation in the “Three-North” region of China. Meanwhile, the uncertainty of renewable energy generation (REG) and demand side management (DSM) pose challenges to the optimal operation of the whole system, leading to difficulty in improving the economic benefits of the whole system. In order to mitigate the above problems, wind generators, solar generators, CHP units, conventional generating units, interruptible loads, battery energy storage systems (BESSs), as well as electric vehicles (EVs) are aggregated into a virtual power plant (VPP) as a community of interest. Particularly, heat storage tanks (HSTs) are introduced into VPP in this paper to realize the decoupling of heat and power of CHP units and enhance the flexibility of the VPP. Firstly, the composition and structure of the VPP are described. Secondly, a day-ahead optimal dispatching model of the VPP considering heat output is established. Finally, the effectiveness of the proposed model is verified by the modified IEEE30 bus system.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources, which have become a new operation mode with substantial advantages such as high flexibility, adaptability, and economy. This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources. The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments. In this regard, the faults of stochastic optimization and traditional robust optimization can be overcome. Firstly, a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed, and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets. Then, an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem, which improves the computational efficiency of the model. Finally, the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy. They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

Storage-Coupled Nuclear Combined Cycle

AbstractA new design paradigm for nuclear power plants is needed to complement the increasing adoption of low marginal cost variable renewable energy resources. The situation is reflected in the wholesale electricity price–duration curve with four distinct economic opportunities: (a) a hundred or so hours per year of high-value peaking power; (b) about 4000–5000 h of moderate electric prices; (c) about 2000 h per year when renewables set the marginal price at or near zero; and (d) about 1000 h of flexible ramping between the b and c regions. The current approach to the low-carbon energy transition reduces the need for baseload power and requires curtailment of conventional, nuclear, and even renewable generation, decreasing their capacity factors and increasing their fixed charges for electricity generation. Flexible low-carbon dispatchable power plants capable of daily cycling along with storage and time shifting of low-cost nondispatchable renewable power will be needed. Although nuclear power plants have demonstrated load-following capability, cycling can be limited by reactor kinetics (xenon poisoning) as well as by thermal stresses and fatigue considerations in the steam cycle. Storage of nuclear heat is hampered by the relatively low operating temperatures of existing nuclear reactors (but not advanced reactors) that lowers thermal to electric conversion efficiency, which in turn increases the required quantity of storage medium and the cost of storage. The quantity of storage medium can be reduced by integration of thermal energy storage with high-grade heat as in the liquid salt combined cycle (LSCC). The LSCC uses high-temperature gas turbine exhaust heat to increase the electricity output per unit of storage medium, uses the stored energy to add operating flexibility to a bottoming steam cycle, and substantially reduces the fuel heat rate. The low fuel heat rate improves economic competitiveness compared to alternative gas turbine-based power plants, especially when burning expensive fuels such as hydrogen. LSCC could be coupled to a nuclear power plant for time shifting both nuclear and renewable electricity and could support high utilization of a co-located hydrogen electrolysis plant. Further cost reduction could be achieved by using solid media for thermal energy storage, with the liquid salt used as a heat transfer medium.

Chile's electricity markets: Four decades on from their original design

It has been almost 40 years since Chile reformed its electricity sector. In 1982, the Electricity Act created an energy market for generators and large customers to negotiate supply contracts. It also established a centralized dispatch of power plants in ascending order of generation cost, independent of their owners' supply contracts. This setup results in power exchanges between generators, which are valued using the system's marginal costs. This paper: (i) describes the market design; (ii) shows its evolution to date; (iii) describes the triggers for change; (iv) draws policy lessons and (iv) provides a preliminary assessment of the reform.

Empirical greenhouse gas assessment for flexible bioenergy in interaction with the German power sector

Wind and solar power are already the major pillars of renewable power generation in Germany and will become even more dominant in the future. At the same time, dispatchable power plants phasing out. The expected increase of fluctuations in the residual load could be partial balanced by flexible bioenergy. However, there is currently no assessment approach for quantifying the systemic GHG impacts for flexible bioenergy generation.Based on the merit order concept, we develop an empirical approach to systemically assess the GHG emissions impact for electricity generated by flexible bioenergy plants. We estimate price response functions using the historical data of market prices and feed-in time series for the different forms of dispatchable non-renewbale dispatchable generation (NRDG). By calculating the expected NRDG based on these functions, and using specific emission factors, we are able to estimate the net impact for a switch from invariable to flexible bioenergy generation.The calculated net impact ranges from −20 to −36 g CO2eq per kWh, which is equivalent to a benefit of −10% to −18% respectively for an average carbon footprint of 200 g CO2eq per kWh. The calculation tools are written in Python and freely accessible on ZENODO and GitLab.

Least-Cost Non-RES Thermal Power Plants Mix in Power Systems with Majority Penetrations of Renewable Energy

The ambitious targets of the European Union (EU) for a greater penetration of renewable energy sources (RES) in all areas of activity have led to power systems with growing levels of variable RES (VRES) all over the EU. Considering these targets, the EU countries presented their National Energy and Climate Plans (NECP) with their expected capacity until 2030. The NECPs considered a relevant increase in the VRES capacity and in some cases a decrease in the capacity of dispatchable power plants. VRES have near-zero marginal costs and increase the volatility of the net-load due to the stochastic profile of their production. These characteristics increase the need to maintain fast-response dispatchable power plants to guarantee the security of supply and also decrease market prices. Thus, governments promote externalities, as capacity mechanisms and other incentives to these players, guaranteeing their economic sustainability. This study presents the optimization of the non-RES thermal capacity of the Iberian power system by 2030, considering the least-cost algorithm. Considering a cooperative scenario between Portugal and Spain, it is possible to reduce the system costs by 17.40%, the curtailments quantity by 21.93%, the number of market-splitting hours by 43.26% and the dioxide carbon emissions by 4.76%.

Optimal Dispatch of Renewable and Virtual Power Plants in Smart Grid Environment through Bilateral Transactions

In a competitive power market, assessment of optimal bilateral transactions is very important for achieving more financial benefits without any stability, security and reliability issues. Due to the open access transmission network and the wide deployment of renewable energy sources (RES) and virtual power plants (VPP), dispatch of VPP and RES units through bilateral transactions become complex. This paper presents an efficient approach for the determination of optimal and feasible bilateral transactions among the power traders in a smart grid based on proposed factors, i.e., active participation factor (APF), maximum loading point (MLP) and load distribution factor (LDF). The objective of the proposed tri-level dispatch model is to minimize the cost of generation and transmission cost of transactions by using a mixed integer nonlinear programming (MINLP) technique. The proposed market dispatch model efficiently gets rid of the congestion in the transmission network and enhances the power transfer capability of the network, i.e., transaction dispatch capability of the network. The performance of the proposed tri-level model has been validated on the modified IEEE-14 bus system and modified IEEE-30 bus system.

Optimal dispatch of multi-virtual power plants based on Grey Wolf Optimization algorithm

The day-ahead scheduling of virtual power plant (VPP) is of great significance for the development of distributed generations. To solve the problem of large-scale VPP scheduling, this paper proposes a multi-VPPs optimal dispatching strategy. In this model, the VPP is divided into multiple sub-VPPs, and each sub-VPP scheduled internally. Likewise, the power interaction between each sub-VPP at the contract price is allowed. This model can realize complementary power, and hence maximize economic benefit of VPP and also improve the level of new energy consumption. Then, to solve this nonlinear optimization problem, Grey Wolf Optimization (GWO) algorithm is employed. Finally, the proposed model is tested through a detailed case study, and results demonstrate that the GWO algorithm can improve the computational efficiency.

Economic-Emission Dispatch Problem in Power Systems With Carbon Capture Power Plants

Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this article, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multiobjective optimization framework. The power system's flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The dc power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.

A vision of flexible dispatchable hybrid solar‐wind‐energy storage power plant

Dispatchable renewable generation is essential for 100% renewables power system. It is in the interest of both power system operators and customers since it reduces the need for flexibility and reserve. To this end, battery energy storage systems (BESS) are proposed for integration in the renewable power plant. This paper presents the optimal dispatch unit for a dispatchable hybrid solar-wind power plant with BESS framework. It achieves optimal dispatchable renewable generation (from dispatchable hybrid renewable (solar-wind) power plant with BESS, DHRB, operator perspective), subject to operational limits, by exploiting the synergy of wind and solar energy and combining it with storage capability of BESS using two different operation strategies, maximisation of revenues and maximisation of renewables harvesting. A continuous BESS degradation model is incorporated in the proposed rolling-algorithm-based-optimal dispatch unit to improve the accuracy of results. The applicability of the proposed methodology and the performance of the operation strategies are demonstrated using a case study and the operation strategies are compared. Further, the effect of BESS size on its degradation and dispatchable power in a hybrid solar-wind power plant with BESS are investigated through BESS lifetime. An indicative economic analysis is carried out to provide ground for the extra investment on and sizing of BESS for a dispatchable hybrid renewable power plant.

Comparing CO2 emissions impacts of electricity storage across applications and energy systems

Summary Electricity storage systems can support the decarbonization of energy systems. However, the effect of electricity storage use on greenhouse gas emissions is complex because of roundtrip efficiency losses of the storage and its effects on the dispatch of different electricity-generation technologies. The limited literature examining this issue focus on individual electricity storage applications and the geographies where they are applied. Here, we systematically compare the effects of electricity storage on CO2 emissions across four applications in electricity systems resembling seven European countries. Our findings reveal large emission impact differences between applications and countries. Among other findings, we find that when compared with wholesale arbitrage, storing electricity in other applications exhibits much lower effects on CO2 emissions. We also investigate different policy options to reduce CO2 emissions from storing electricity. We find that although a higher carbon price can have a substantial effect on reducing the CO2 emissions from wholesale arbitrage, other applications require alternative approaches.