Regional Power System Research Articles

A risk-based stochastic energy-water-carbon nexus analytical model to support provincial multi-system synergistic management – A case study of Shanxi, China

The synergistic management of energy-water‑carbon (EWC) nexus systems is crucial for achieving the sustainable development goals (SDGs). Therefore, a non-deterministic interval chance-constrained fractional optimization model for EWC nexus system (ICCF-EWC) management has been developed in this study. This model is capable of handling uncertain parameters represented as stochastic probability distributions and interval values, providing an effective approach to addressing dual-objective optimization problems. Meanwhile, this model is expected to investigate the effect of water scarcity/carbon abatement pressure on the overall system, and potential synergistic abatement effects. A case study of Shanxi Province shows that over the next 30 years, the cumulative installed capacity for clean and renewable energy will exceed 65 %. The dominance of coal-fired electricity will be considerably diminished, with wind power overtaking coal/gas-fired power by around 30 %. Moreover, water scarcity and carbon mitigation pressure would promote the development of electricity conversion mode to clean energy rather than the large-scale carbon capture and storage (CCS) technology upgradation of thermal power. The results can help support low-carbon transition of power systems at a province-level in China and financial incentives related policy making to advance water conservation and carbon emission mitigation. The developed model can also be adapted to other energy resource-dependent regional power systems.

Improving the quality of the power supply system of technical fleet vessels when supplied from shore regional electric power systems

A significant increase in the load on technical fleet objects, such as floating cranes, dredgers, dredgers, etc., causes the problem of electromagnetic compatibility. Electricity supply of technical fleet vessels is carried out from 0.4 kV shore networks for economic reasons, but the quality of electricity of the shore-to-ship power transmission does not meet the requirements of GOST 32144-2013 and the Rules for the Classification and Construction of Inland Navigation Vessels of the Russian River Register (RSRR). Physical wear and tear of technical fleet vessels exacerbates this problem. Subject of the study: processes affecting the stability of the electrical load on the voltage of the ship's 0.4 kV network when supplying the vessel with electricity from a 10 kV shore network and violating the EMC of technical equipment. Objective of the study: development of scientific provisions and recommendations to improve the stability of ship voltage load nodes when powered from shore networks. Research subject: power supply systems of technical fleet vessels when powered from shore power systems. Research results: based on the conducted research, it is shown that the proposed method increases the stability of the electrical load on the voltage of technical fleet vessels when powered from shore networks, which is essential for water transport.

Capacity Optimization Allocation of Multi-Energy-Coupled Integrated Energy System Based on Energy Storage Priority Strategy

As the global focus on environmental conservation and energy stability intensifies, enhancing energy efficiency and mitigating pollution emissions have emerged as pivotal issues that cannot be overlooked. In order to make a multi-energy-coupled integrated energy system (IES) that can meet the demand of load diversity under low-carbon economic operation, an optimal capacity allocation model of an electricity–heat–hydrogen multi-energy-coupled IES is proposed, with the objectives of minimizing operating costs and pollutant emissions and minimizing peak-to-valley loads on the grid side. Different Energy management strategies with different storage priorities are proposed, and the proposed NSNGO algorithm is used to solve the above model. The results show that the total profit after optimization is 5.91% higher on average compared to the comparison type, and the pollutant emission scalar function is reduced by 980.64 (g), which is 7.48% lower. The peak–valley difference of the regional power system before optimization is 0.5952, and the peak–valley difference of the regional power system after optimization is 0.4142, which is reduced by 30.40%, and the proposed capacity allocation method can realize the economic operation of the multi-energy-coupled integrated energy system.

Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach

IntroductionThis study addresses the challenge of active power (AP) balance control in wind-photovoltaic-storage (WPS) power systems, particularly in regions with a high proportion of renewable energy (RE) units. The goal is to effectively manage the AP balance to reduce the output of thermal power generators, thereby improving the overall efficiency and sustainability of WPS systems.MethodsTo achieve this objective, we propose the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device within WPS power systems. The TLDDQN method leverages the benefits of transfer learning to quickly adapt to new environments, thereby enhancing the training speed of the double deep Q-network (DDQN) algorithm. Additionally, we introduce an adaptive entropy mechanism integrated with the DDQN algorithm, which is further improved to enhance the training capability of agents.ResultsThe proposed TLDDQN algorithm was applied to a regional WPS power system for experimental simulation of AP balance control. The results indicate that the TLDDQN algorithm trains agents more rapidly compared to the standard DDQN algorithm. Furthermore, the AP balance control method based on TLDDQN can more accurately manage the storage device, thereby reducing the output of thermal power generators more effectively than the particle swarm optimization-based method.DiscussionOverall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.

Integrated resource strategic planning considering inter-regional flexibility supply–demand balance: A case study for the Northwest and Central Grid in China

China’s rapid advancement in renewable energy and regional power grid interconnection has highlighted the need for enhanced system flexibility to ensure the security and stability of regional power systems. To address this issue, this study presents a new framework for integrated resource strategic planning for inter-regional flexibility (IRSP-IF). This framework evaluates inter-regional flexibility demand and optimizes power resource allocation across regions. The northwest and central grids in China are used as case studies to evaluate inter-regional power resource allocation from 2022 to 2035. This analysis considers key factors including flexibility supply–demand balance constraints, regional power grid interconnections, and time scales for assessing flexibility demand. The results show that incorporating these constraints and interconnections into the strategic power plan enables greater integration of renewable energy while reducing both installed capacity and the overall cost of regional power resources. In the S3 scenario, which includes inter-regional flexibility supply–demand balance, the total integration of wind and solar power increases by 1.54%. Additionally, the combined average annual fixed and operating costs decrease by 1.44 billion yuan compared to the S2 scenario, which only considers single-region flexibility supply–demand balance. Furthermore, evaluating flexibility demand at a shorter time scale results in a higher proportion of flexible resources, with an average annual growth of 24.43%.

Review of Methods for Improving the Maneuverability of Power Plants with an Increase in the Share of Wind and Solar Power Plants in Regional Power Systems

Review of Methods for Improving the Maneuverability of Power Plants with an Increase in the Share of Wind and Solar Power Plants in Regional Power Systems

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

A Low-Frequency Oscillation Suppression Method for Regional Interconnected Power Systems with High-Permeability Wind Power.

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system.

The optimization and dispatch of regional multi-energy complementary power system in green certificate Trading - Carbon emission trading

Abstract This proposal of the “double carbon” goal underscores the pressing need to reduce carbon emissions. To address this issue, the utilization of an optimal scheduling model for an integrated multi-energy complementary system, comprising wind, solar storage, and thermal power generation, is proposed. Two renewable energy generating units, wind and photovoltaic power, cooperate with thermal power units to generate electricity to meet urban base load requirements. Energy storage units compensate for the instability of power generation from renewable energy units. To significantly decrease system carbon emissions, a green certificate trading-carbon emission trading system is introduced. The carbon emission reduction facilitated by the green certificate can offset a portion of the carbon emissions, and the quantities of tradable green certificates and carbon emissions in their respective markets are adjusted accordingly. This model aims to maximize the system’s comprehensive operating earnings while considering the low-carbon economy aspect. The proposed model undergoes simulation analysis, and the results demonstrate its reasonableness and efficacy in terms of carbon reduction and economic viability.

The frequency analytical method for regional power systems considering the emergency frequency control

With the continuous construction of high-voltage direct-current (HVDC) transmission projects, higher requirements are placed on the frequency security of the regional power systems. In order to ensure the frequency security of regional power systems, this paper proposes a transient frequency analytical method considering the emergency frequency control (EFC). Firstly, the aggregated system frequency response (SFR) model is constructed by reducing the order of the generator governor equation, the EFC equation, and the load model equation. Then, based on the aggregation model, the analytical solution of the transient frequency nadir of the regional power system is derived. The model can quickly and accurately calculate the transient frequency nadir of the regional power system considering EFC after the HVDC block fault. Finally, based on an actual regional power system model, the accuracy and applicability of the proposed method under HVDC block fault are verified.

Features of multi-agent evaluation of of microgrid systems efficiency in parallel operation with the power system according to the reliability criterion

Ensuring reliable electricity supply to consumers in the face of destruction of critical energy infrastructure and shortage of generating capacities in Ukraine requires the development of distribution systems and their management systems. The purpose of the study was to substantiate the development of models for ensuring the optimal structure of electrical distribution networks under the conditions of connecting micro-networks according to the reliability criterion in conditions of limited investments. The paper uses a method for assessing the structural reliability of complex electrical systems with microgrid structures and forms a rational power distribution of such structures according to the criterion of optimising the reliability indicators of the studied electric power system. A mathematical optimisation model based on a computational system was proposed, designed to solve non-convex problems with minimising integral reliability indicators, considering financial constraints and the investment efficiency curve. Based on the research, the possibilities of optimisation using the BARON solver available on the NEOS server were examined. The results of the model’s performance are demonstrated using examples, considering the parameters of distribution system objects and their limitations on network components. An algorithm and programme for solving the problem of targeting power flows of microgrid structures in multi-node regional power systems are proposed. Algorithms and scenarios for the response of dispatching services are developed, provided that investments are limited, which will ensure the survivability of the power system as a whole. It is established that the development of rational electricity flows of microgrid structures will minimise the under-supply of electric energy by specific load nodes and determine their shares in covering the demand of the energy island in conditions of power shortage. The findings can be used in the operational management of power systems

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72 %, and the daily battery charge/discharge life loss is minimized to 0.00036 %. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56 %-7.26 %, Spread metrics are better by about 0.39 %-2.73 %, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

Carbon Emission Measurement Method of Regional Power System Based on LSTM-Attention Model

With the acceleration of the green and low-carbon transformation of the power system, it is very important to calculate and analyze the carbon emissions of the urban power system. In order to effectively grasp the carbon emission distribution of power system and reduce the carbon emission of power system, this paper proposes a systematic carbon emission measurement method for regional power system. Firstly, the quantitative analysis model of driving factors for regional power system carbon emissions is constructed, and the direction and measures of low-carbon transformation and green collaborative development of regional power system are proposed. Secondly, energy consumption scenarios under different constraints are established to support the collaborative control path of CO2. It provides key data and a theoretical basis for the low-carbon development of the power industry. Finally, a systematic carbon emission measurement method based on LSTM-Attention is proposed. Finally, the effectiveness of the proposed method is verified by an example analysis, and the effective path of carbon emission reduction in the future is analyzed.

Loss of load probability for power systems based on renewable sources

An increasing number of small and variable power sources, such as wind and solar power plants, are being integrated into power systems, potentially affecting their reliability. This study was aimed at developing a method for evaluating reliability in terms of the loss of load probability. To this end, an algorithm capable of assessing the expanded role of renewable sources in the power system was established, which could take into account the specific roles and features of wind, solar, and hydro power plants, including pumped storage hydro power plants. Various models were developed and tested to closely emulate real power system behavior. A regional power system was evaluated over multiple years. The results indicated that the reliability of power systems fluctuates with the available capacity of renewable resources. These findings provide a foundation for a more precise specification of power reserves in such systems to prevent excessive reduction in reliability.

Economic Dispatch of Multi Regional Power Systems Based on CMOPSO Algorithm

With the progress of society and the aggravation of environmental pollution, the economic dispatch of the power system is developing towards multiple environmental and economic goals. To improve energy utilization efficiency, this study innovatively proposes a multi-objective particle swarm optimization algorithm based on competitive learning, and uses this algorithm to solve multi regional environmental and economic scheduling problems. In addition, the study solves static and dynamic economic (S-DE) scheduling problems in multiple regions through improved competitive group optimization algorithms. The research results show that under different testing systems, the average distribution uniformity indicators of the research algorithm built on competitive learning are 0.8058 and 0.8457, and the average anti generation distance is 67.6316 and 1664.0978. The improved competitive group optimization algorithm solves the maximum, minimum, and average fuel costs for static economic scheduling in multiple regions, which are 656.2243 $/h, 655.8592 $/h, and 655.9866 $/h, respectively. Thus, the designed algorithm can effectively solve economic scheduling problems, which is of great significance for resource integration, saving power generation costs, and reducing pollution emissions.

A low-carbon evaluation framework for regional power systems

With the escalating challenges of climate change and the imperative for carbon neutrality, evaluating the carbon footprint of regional power systems becomes crucial. To address this issue, a low-carbon evaluation framework for regional power systems is developed. First, this study identifies the fundamental requirements that the index system for the low-carbon power system should follow and specifies the general direction for the index system’s creation. Second, an evaluation model for carbon reduction ability is proposed by considering generation side, grid side and load side of the power system. Then, a comparative evaluation model for the carbon reduction capability of multi-regional power systems is proposed to spatiotemporally compare the carbon reduction capability of different power systems. Next, a quantification method for the weights of low-carbon evaluation indicators is established based on the power system simulation of multi-scenario scheduling and index sensitivity analysis method. Finally, a low-carbon comprehensive score for regional power systems is created by combining the evaluation results of the system’s carbon reduction capacity with index weights. The practical research results in a region of Zhejiang province in China demonstrate that the proposed model can provide a reasonable and feasible evaluation plan for the low-carbon construction of regional power systems.

Applying green learning to regional wind power prediction and fluctuation risk assessment

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems.

Assessing the carbon emission reduction effect of flexibility option for integrating variable renewable energy

Integrating a high-penetration level of variable renewable energy into power systems requires the pro-rata deployment of flexibility technology in the recent trends of low-carbon transition. This study proposes a multi-regional power system optimization model to assess the carbon emission reduction effect of different flexibility options in the Chinese power sector, including total carbon emissions, peaking timetables and values, carbon emission transfer between regional power systems, and carbon emission source changes between technologies. The results reveal that the combinations of dispatchable generation, inter-regional transmission, energy storage, and demand-side response can significantly reduce carbon emissions from power systems, but will lead to higher expenditure. Carbon emissions would peak 1–6 years in advance under the integrated flexibility option consisting of all flexibility technologies, and the corresponding peaking value would decrease by 0.26–0.48 billion tonnes compared to other configuration scenarios. However, this does not apply to all the regions. The coupling relationship between the flexibility option and carbon emission reduction in regional power systems changes with the disparities in power demand, system structure, and renewable energy consumption targets.

A fuzzy approach to the regional electric power system's stability monitoring based on socially available information

Subject. This article deals with the issues related to the stability of the region's electricity system. Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system. Methods. For the study, we used a fuzzy logic approach. Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial function reflecting the change in the parameter of the load on the system over time. Conclusions and Relevance. The consumption indicator over time is unstable, prone to sharp changes both negatively and positively, which may be due to the specifics of the formation of demand for electricity, where the consumer's decision is of key importance. The results of the study can be used to develop strategies for regional electricity consumption systems, and can also be implemented in the practice of specific electric power enterprises as part of making forecasts for energy consumption.

Forecasting Peak Hours for Energy Consumption in Regional Power Systems

. Electrical power is the second most important commodity in electrical energy markets. For consumers, the charged amount of “generator” power is determined as the average value of hourly consumption amounts on working days during peak hours in the region. The cost of power in some regions can reach 40 % of the final tariff, so reducing the load during peak hours by 10 % can lead to a decrease in monthly consumer payments by 3 %. However, such a way of saving money is not available to the consumer since the commercial operator of the wholesale market of electricity and capacity publishes the peak hours of the regions after the 10th day of the next month, when this information is no longer relevant. Timely forecasting of peak hours will make it possible, on the one hand, to reduce consumer costs for payments for electric power, and on the other hand, to smooth out the daily schedule of electric load of the power system, thereby optimizing the operation of generating equipment of stations and networks of the system operator. The article presents a study of the effectiveness of machine learning methods in the context of forecasting the peak hour of a regional power system. The study concerns the period from November 2011 to October 2023, covers 76 regions of the Russian Federation, including subjects of price (1st and 2nd) and non-price zones and includes 10 machine-learning methods. The results of the study showed that statistically, the K-nearest neighbors clustering method turns out to be the most accurate, although not universal. Support Vector Classifier and Decision Tree Classifier have demonstrated high efficiency (in terms of accuracy and speed). The study also refuted the assumption that the closest data in terms of time series has the greatest value in predicting peak hours.