A new flexibility based probabilistic economic load dispatch solution incorporating wind power

Guest Editorial: Situational awareness of integrated energy systems

Operational flexibility is an important property of electric power systems. The term flexibility is widely used in the context of power systems although at times without a proper definition. The role of operational flexibility for the transition of existing power systems, many of them based on fossil fuels, towards power systems effectively accommodating high shares of variable Renewable Energy Sources (RES) has been widely recognized. Availability of sufficient operational flexibility is a necessary precondition for the grid integration of large shares of power in-feed from variable RES, for example wind power and photovoltaics (PV). The paper analyzes the role of operational flexibility in power systems. Necessary flexibility metrics for categorizing different types of operational flexibility are discussed. A new methodology for assessing the technically available operational flexibility is presented. Qualitative insights are derived, notably regarding the limits of RES integration for a given power system with its specific flexibility properties. An extensive simulation study is performed, assessing the role that operational flexibility has for the mitigation of challenges, namely curtailment, arising from high shares of variable RES in-feed.

In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.

The escalating grid-connected capacity of renewable energy sources, predominantly wind and photovoltaic (PV) power, along with its inherent volatility and anti-peaking attributes, exacerbates the peaking demands on the power system. Consequently, this trend necessitates enhanced flexibility in power system peaking capabilities. In response to this challenge, this paper introduces an optimal scheduling methodology grounded in a two-stage stochastic model tailored for power systems, which incorporates thermal-storage peaking pricing. Initially, a hierarchical decision-making framework, employing the group decision hierarchy analysis method, is devised to formulate a peaking pricing strategy for thermal power and energy storage. Subsequently, acknowledging the variability of wind power, multiple scenarios are generated and consolidated through the application of k-means clustering. A two-stage stochastic optimization approach is then utilized for day-ahead pre-dispatch of thermal power and storage units, and intraday dispatch adjustments are made to accommodate the uncertainties associated with renewable energy sources by leveraging flexible resources, thereby ensuring power balance, and maximizing wind power integration. The efficacy of the proposed approach is validated through simulations conducted on the enhanced IEEE39-bus system. The numerical results show that the flexibility of the system can be effectively improved, and the wind power and photovoltaic accommodation rate is increased by 98.11 and 99.42%, respectively.

This paper deals with dynamic combined economic emission dispatch problem of an electric power system consisting of thermal, hydro and photovoltaic (PV) power plants. Mathematical models of these plants as well as the model of dynamic combined economic-environmental nonlinear optimization problem are given. The proposed optimization model was implemented in publicly available GAMS software, and the result is the optimal load distribution among the plants during a one-day optimization period. The software is applied to the power system without PV power plants and with a number of new PV plants added to the existing hydrothermal system. The results quantify the positive effects of PV power plants on both fuel cost and greenhouse gas reduction and recommendations are given related to the application of PV generation in modern systems. Software efficiency in solving both dynamic economic dispatch (DED) and dynamic combined economic emission dispatch (DCEED) problems is discussed, and the conclusions are drawn about its application possibilities.

The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.

At present, the proportion of new energy sources such as wind power and photovoltaic in the power system is gradually increasing, flexibility has gradually become one of the important evaluation criteria for the safe and stable operation of current and future power systems. As a power system measure of its ability to respond to uncertain events, reasonable quantification and analysis power system’s flexibility is a prerequisite. This paper, quantified the power system flexibility under two time scales of 15min and 1h, and then two flexibility evaluation indicators are proposed for ramp flexibility gap and peak-shaving flexibility gap. Finally, according to the actual data of a regional power grid, different time scales are selected, and the flexibility quantitative model of this paper is applied by random production simulation technology, and the flexibility of the power grid is quantified and analyzed, which verifies the effectiveness of the proposed model and puts forward prevention suggestions for the period of insufficient flexibility.

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.

Renewable energy sources are currently presented as an economically viable and environmentally safe option in the near future. A major constraint to the incorporation of wind and solar generation at large scale is the increase of variability in the power system. To assure the perpetual balance between power production and gross consumption a significant improvement on power systems flexibility is required. Such flexibility in the power system can be achieved by two options on the demand side through demand response obtained through industrial processes: Storage and Substitution. The power system model in study contemplates the purchase of electricity from the Dutch Balancing Market. The electricity prices of the Balancing Market are considered unpredictable. The storage system is characterized by the size of the storage tank and by ramp up/down rates, reflecting the changing speed of the production levels. The substitution system is characterized by the ramp rate of substitution between electricity and an alternative energy carrier as input. The impact of the parameters on the Power System Flexibility when connected to the balancing market under several scenarios was analyzed by Linny-R, a software tool that applies Linear Programming optimization. For the storage system a bigger tank size, a higher ramp rate and a high level of predictability will increase the flexibility of the system. As the actual predictability of the balancing market is limited, the flexibility is limited too, which makes the storage system a questionable option. For the substitution system flexibility is increased by a higher ramp. The effect of the predictability is less dominant, which makes substitution a suitable flexibility enabler for the current Dutch market system. In this context, a restructure of the energy markets, considering the prices predictability, is suggested, as a way of easing the penetration of renewable energy sources.

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

An accurate forecast of wind power plays an increasingly important role in economic dispatch and power system operations. Nevertheless, the randomness, intermittence, and volatility of wind power pose great challenges in power system balance. To improve the forecasting accuracy of wind power, a multi-scale fusing deep learning method is proposed to forecast short-term wind power for multiple wind turbine generators in a wind farm. The inherent spatio-temporal characteristics are studied by dividing the time scale into a small-scale nearby part and a large-scale historical part. The fine-grained dynamic characteristics in the small-scale nearby part are extracted using stacked three-dimensional convolutional neural networks (3D-CNN), and the steady-state characteristics in the large-scale historical part are extracted using Spatio-Temporal Long Short-Term Memory (ST-LSTM) units. Then, wind power for multiple wind turbine generators can be forecasted by fusing the two deep learning channels. An actually collected wind power dataset is used to validate the effectiveness of the proposed method, which is exported from 33 wind turbine generators in a wind farm located in a northwestern province of China. The experimental results show that the proposed wind power forecasting method can improve the forecasting accuracy effectively when compared with the state-of-the-art wind power forecasting methods.

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

High penetration of variable renewable sources act as a heavy burden on conventional power system management and operation. Uncertainty in power systems expanded from demand side to generation side as well. Since new sources of imbalances have entered power system, it should be reorganized, automated and modernized. New providers of flexibility should be recognized and used in future power system planning and design. One of the possible technologies that can be used for flexibility provision are electric vehicles. Numerous fast charging stations are installed all over the world and such trend will continue in future. Depending on their operation, charging stations can act as flexibility providers but they can also further degrade system's flexibility if installed without any kind of energy buffer. This paper will present mixed integer linear model for flexibility studies of modern power systems with high penetration of variable renewable sources and electric vehicles. Results clearly show that smart planning of fast charging infrastructure can bring huge benefits to power system concerning costs, emissions, and variable renewable power curtailment.

An efficient and reliable evolutionary based meta-heuristic approach, termed as simulated annealing, is presented for the solution of optimal economic power dispatch. The generation of electricity from the fossil fuel releases several contaminants, such as sulphur oxides, nitrogen oxides and carbon dioxide, into the atmosphere. Simulated annealing algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The advantages of the approach in terms simulated annealing algorithmic problem reduction, flexibility and solution methodology are also discussed. In the Multi-objective problem formulation, four important non-commensurable objectives in an electrical thermal power system are considered. These are economy and environmental impacts because of NOx, SO2, CO2 emissions. The feasibility of the proposed method is compared with a simple genetic algorithm is described to multi-objective optimal economic dispatch of electrical power systems.