A Distributed Robust Dispatch Approach for Interconnected Systems with a High Proportion of Wind Power Penetration

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

Giving consideration to the bidirectional uncertainty of wind power output and load, in order to improve the accuracy of reactive power optimization of asset allocation, the paper combines the wind power output scenario with complete-bus load clustering method in distribution network, and establishes the selection model of system operation scenario. This method can accurately select the system operation status, comprehensively consider the randomness of load and wind power output, and effectively configure reactive power compensation capacitor for distribution network containing wind power generators. The validity of the method is verified by IEEE 33 bus distribution system.

With the in-depth development of smart grid, renewable energy, such as wind power, will be an important source of electrical energy, the proportion of renewable resources increase rapidly. Serious problems emerged with large-scale wind power integrating to power grid. The integration of wind generation imports variability and uncertainty to system operation and control. Control center must consider this variability and uncertainty to dispatch economically. Fortunately, there will be many electric vehicles in smart grid. With the popularization of electric vehicles, charging stations and electric vehicle battery swap stations will be increasing rapidly. Battery swap stations can be regarded as energy storage power stations, which can be used to stabilize the wind power output variability and uncertainty. In this paper, new economic dispatch model considering wind power and electric vehicle battery swap stations is proposed, the Particle Swarm Optimization (PSO) method and prior priority way are adopted to solve this model. The algorithm based on MATLAB 6.5 is developed. The correctness and effectiveness of the model and algorithm are verified using test system.

Considering the uncertainty of wind power output and the market price of electric energy and frequency modulation auxiliary services, a model is established. The established model adopts a two-stage collaborative operation optimization method. The first stage aims to maximize wind power consumption and lowest cost, and the second stage aims to maximize the profitability of idle energy storage system under the energy market and frequency regulation market. The proposed strategy is obtained through the analysis of the calculation example. The energy storage system not only reduces the fluctuation of new energy and improves the dispatchability and absorption capacity of wind power, but also can increase the operating income in the environment of the energy market and ancillary service. Especially when the price of frequency regulation market is high, it has good economic benefits and reduces the cost recovery period of investment in the energy storage system.

In recent years, new energy has achieved rapid development in the whole world. However, with the gradual increase in the installed capacity of new energy, because of the uncertainty and volatility of new energy output, new energy has brought many impacts and challenges to the operation of the power system. In order to guarantee the safe and stable operation of the power system, the power system has to provide sufficient flexibility to cope with the volatility and uncertainty of the new energy output. Improving the prediction error accuracy of new energy output can effectively reduce the uncertainty of new energy output, so as to reduce the demand for power system flexibility, reduce the operating cost, and improve the system operation reliability. This paper utilizes the data mining technology to establish the prediction error identification module. Based on the data feature extracted from the wind power prediction input data, the potential abnormal wind power prediction error can be identified, and the original wind power output prediction result can be corrected, so as to improve the predication accuracy of the wind power generation. At the same time, in the prediction and identification process, the wind speed prediction data and historical wind power output data of neighbouring regional wind farms will also be utilized to improve the prediction accuracy of wind farm output in this region. The predicated time scale is 12 hours ahead of time, and the predicted result is the wind power output of the next 12 hours (a predicted resolution of 1 hour, and a total of 12 predicted values).

This study examines the issue of wind power smoothing in renewable-energy-grid integration scenarios. Under the “dual-carbon” policy initiative, large-scale renewable energy integration (particularly wind power) has become a global focus. However, the intermittency and uncertainty of wind power output exacerbate grid power fluctuations, posing challenges to power system stability. Consequently, smoothing strategies for wind power energy storage systems are desperately needed to improve operational economics and grid stability. According to current research, single energy storage technologies are unable to satisfy both the system-level economic operating requirements and high-frequency power fluctuation compensation at the same time, resulting in a trade-off between economic efficiency and precision of frequency regulation. Therefore, hybrid energy storage technologies have emerged as a key research focus in wind power energy storage. This study employs the SE-SGMD method, utilizing the distinct characteristics of lithium batteries and supercapacitors to decompose frequency regulation commands into low- and high-frequency components via frequency separation strategies, thereby controlling the output of supercapacitors and lithium batteries, respectively. Additionally, the GA-GWO algorithm is applied to optimize energy-storage-system configuration, with experimental validation conducted. The theoretical contributions of this study include the following: (1) introducing the SE-SGMD frequency separation strategy into hybrid energy storage systems, overcoming the performance limitations of single energy storage devices, and (2) developing a power allocation mechanism on the basis of the inherent properties of energy storage devices. In terms of methodological innovation, the designed hybrid GA-GWO algorithm achieves a balance between optimization accuracy and efficiency. Compared to PSO-DE and GWO-PSO, the GA-GWO energy storage system demonstrates improvements of 21.10% and 17.47% in revenue, along with reductions of 6.26% and 12.57% in costs, respectively.

Due to its unique uncertainty and volatility, wind power is increasingly penetrating various levels of power grids, and its impact on the stability of power grid operations is gradually increasing. In order to cope with the uncertainty of wind power output and to improve the stability of system operation, a robust optimization and scheduling model for the integrated electricity-gas-heat system (IEGHS), and heat considering the DC power flow security domain is proposed. Firstly, based on the traditional DC power flow security domain model, a DC power flow security domain model based on wind power uncertainty is established and the operating safety boundary of nodes is established. Secondly, considering the security domain and wind power uncertainty, a robust budget parameter is introduced, and a controllable robust optimization and scheduling model for IEGHS is proposed. Finally, the effectiveness and practicality of the proposed model are demonstrated through numerical simulation. In summary, the proposed model takes into account the uncertainty of wind power and the safety domain of the DC power flow, and introduces robust budget parameters to achieve a controllable robust optimization and scheduling for IEGHS. The simulation results demonstrate the effectiveness and practicality of the proposed model.

A low‐carbon economic dispatch model for electricity market with wind power based on improved ant‐lion optimisation algorithm

Wind Power Plant (WPP) is part of renewable energy sources, with rapid expansion worldwide. It has the advantages of clean and green energy, but its uncertainty leads to an additional grid integration cost. The uncertainty of wind power output is much dependent on the accuracy of the wind power forecast (WPF) result. Since there is no perfect wind power forecast, understanding the current system's forecast accuracy characteristics is essential in expecting typical errors faced in the future. This paper proposed a new algorithm of the statistical approach method to evaluate characteristics of wind power forecast errors (WPFE) from an observed power system with high-penetration WPP. This method combined the approach of scatter diagram, statistical distribution, standard error performance, and score weighting in a multi-stage algorithm. It consists of serial and parallel processes to check the consistency of the results. In this study, a comprehensive analysis was made of various scenarios based on location and timescale. This proposed algorithm has been successfully tested on statistical data of Sidrap WPP and Jeneponto WPP in the Southern Sulawesi power system. The result showed that the scenario with the aggregation of both WPPs in hour-ahead timescale has the most accurate and consistent performance among all scenarios. It demonstrated specific characteristics of WPFE in the observed power system that can be used as an essential starting point in conducting future wind integration expansion studies.

In today's world, with the rapid development of green and clean energy, the comparison of wind power in power network has been gradually improved. However, wind power has also brought new challenges to the stable operation of power system due to its inherent volatility and randomness. In this paper, the economic dispatching model of power system including wind power grid connection is studied. On the basis of stochastic expectation and chance-constrained programming, the specific economic dispatch model with wind power integration is given. The core of this paper is that we simulate the degree of wind power output fluctuation by setting different confidence levels. Simulation results show that a larger confidence interval can achieve better security, while a smaller confidence interval can achieve better economy.

The electricity supply chain system consists of power plants, transmission and distribution. Differences in power plant characteristics cause differences in fuel costs and emissions. Inaccuracies in scheduling the plant have an impact on the surge in fuel use. This becomes the basis for the optimization of power plant scheduling. Optimization on the generator side is usually using the economic dispatch model. However, the Economic Dispatch Model does not consider optimization on the transmission network. Optimization on the transmission network is needed because there are losses in the transmission network. Losses will be even greater when the distance between the power plant and the customer is getting further away. So if distance can be minimized, fuel costs can be reduced. Minimizing the distance for shipping goods can use the transportation model, but the transportation model cannot be used for electricity distribution. This is because there are differences between goods and electricity. Therefore, this study proposes the Single Echelon Economic Dispatch (SEED) model. This model is a combination of the economic dispatch model and the transportation model. This model is able to do simultaneous optimization between the generator side and the transmission side. The model is applied to the Mahakam system in East Kalimantan, using all PLN’s power plants. Load consists of two types, namely low load and peak load. As a result, in peak load and low load conditions, the P11 power plant produces the biggest losses and emissions. This is because electricity production in P11 has to go through a longer path compared to other plants. The P11 is also a power plant that requires the highest fuel costs

Large scale wind power generation complicated with restrictions on the tie line plans may lead to significant wind power curtailment and deep cycling of coal units during the valley load periods. This study proposes a dispatch strategy for interconnected wind‐coal intensive power systems (WCISs). Wind power curtailment and cycling of coal units are included in the economic dispatch analysis of regional systems. Based on the day‐ahead dispatch results, a tie line power plan adjustment strategy is implemented in the event of wind power curtailment or deep cycling occurring in the economic dispatch model, with the objective of reducing such effects. The dispatch strategy is designed based on the distinctive operation characteristics of interconnected WCISs, and dispatch results for regional systems in China show that the proposed strategy is feasible and can improve the overall system operation performance.

Concentrated solar power (CSP) with thermal energy storage (TES) not only has fast regulation capability, but also can realize the time-shifted utilization of energy. Therefore, incorporating CSP with TES into the power system is beneficial to peak regulation and smoothing out fluctuations in the output of other renewable energy sources. With the gradual expansion of the total installed capacity of CSP stations, we consider the participation of CSP stations in the spot market as price makers and propose the bidding strategy to maximize the profitability of CSP stations. A bi-level bidding model is derived with the upper-level model aiming at maximizing the profit of CSP stations and the lower-level model aiming at maximizing social welfare. An all-scenario-feasible stochastic algorithm (ASF) is introduced to cope with uncertainty of wind power output. The practicality and effectiveness of the proposed bidding strategy in improving the profitability of CSP stations, enhancing social welfare and promoting wind power consumption are further demonstrated based on the improved IEEE 30-bus case and a real system case in Northwest China.

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.

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.