Power Balance Of System Research Articles

Long-term deep reinforcement learning for real-time economic generation control of cloud energy storage systems with varying structures

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures.

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

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.

An assessment methodology for the flexibility capacity of new power system based on two-stage robust optimization

The inherent variability in wind and solar power output presents a significant challenge to the flexibility balance of power systems. This paper introduces an innovative method for evaluating the flexibility capacity of a new power system, employing a two-stage robust optimization approach. Firstly, a power system flexibility supply and demand balance mechanism model is constructed and quantitatively characterized for the power system flexibility shortfalls set. Subsequently, taking into account timescale characteristics and directionality, the time series production simulation technique is applied to establish the effective ramping and flexibility supply distribution model of thermal power and energy storage units, enabling an analysis of the power system's supply regulation capabilities. On this basis, a power system flexibility capacity assessment method is proposed, which divides the system regulation resources into demand set and supply set, and constructs a power system flexibility capacity assessment model to ensure that the maximum system flexibility margin and the lowest operating cost are taken as the optimization objectives under the system security operation constraints. The column and constraint generation (C&CG) robust optimization algorithm is used to decompose the master problem and the max-min dual-layer subproblems for iterative solving, and the optimal capacity of each unit of the system in terms of output and flexibility is derived. Finally, the effectiveness and superiority of the proposed method is verified through case analysis, which shows that the method can improve the flexibility capacity by 14.9% and reduce the operating cost by 15.83% compared with the traditional proportional allocation method.

Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE

Precise prediction of PV power in the short term is crucial for maintaining power system stability and balance. However, the performance of conventional time series prediction models on short-term long series prediction is scarcely sufficient because of the stochastic and turbulent character of PV power data. This work suggests a PV short-term power forecast model based on weather type, AHA-VMD-MPE decomposition reconstruction, and improved Informer combination to tackle this issue. Firstly, a SUM-ApEn-K-mean++ multidimensional clustering method to group the dataset by weather conditions. Then an AHA-VMD-MPE decomposition model is proposed to decompose the historical power data Finally the Informer model is improved and the improved model is utilized to predict the PV power under various weather conditions. The model exhibits great accuracy and stability in short-term PV power prediction, as demonstrated by the experimental results, which were validated using measured data from many PV power plants.

Power quality improvement of unipolar-input-bipolar-output DC transmission system via load power balancing

In DC transmission and distribution systems, both unipolar and bipolar transmission modes exist, and DC transformers used in these systems are also available in either unipolar or bipolar configurations. In actual systems, due to requirements such as economy, land occupation, and reliability, there is a tendency to use a system with unipolar input and bipolar output. However, the bipolar loads, if unbalanced, will lead to increased equipment costs and voltage imbalance, causing power quality problems. This paper defines the Power Unbalance Factor (PUF) to describe the power quality of the studied DC transmission system and presents an improved DC transformer topology based on a power balancing system. This topology realizes bipolar voltage balance and improves the power quality of the DC transmission system when the load is unbalanced. The influence of the proposed solution on the power design of the DC system is demonstrated through theoretical analysis, and its effectiveness for improving the DC power quality is verified by both simulations in MATLAB/Simulink environment and physical experiments. When the power electronic transformer needs to be overloaded, the proposed topology can reduce the design power of the two branches by using the difference power, which is economical.

The balance issue of the proportion between new energy and traditional thermal power: An important issue under today's low-carbon goal in developing countries

The stability of the new power system depends on the balance between controllable flexible resources and uncontrollable uncertain resources. For many developing countries and regions lacking advanced flexible resources, thermal power is the main force in solving the problem of new energy consumption. Finding the balance between new energy and traditional thermal power is the key for today's low-carbon goal. To solve this problem, this paper calculates the power range and climbing rate range of thermal power based on physical principles and actual data, and characterizes the characteristics of wind power, photovoltaic power, load and net load based on the five proposed volatility indicators. Then, a logically clear method for determining the required number of thermal power units based on the volatility indicators of new energy from the mechanism and principle perspective is proposed. Finally, under the premise of meeting the power balance and flexibility balance of the power system, the reasonable ratios between the installed capacity of new energy and the number of thermal power units are calculated in four seasons. The results indicate that the judgment system can provide guidance for the energy structure of developing regions.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Inter‐day energy storage expansion framework against extreme wind droughts based on extreme value theory and deep generation models

AbstractThe worldwide occurrence of wind droughts challenges the balance of power systems between energy production and consumption. Expanding inter‐day energy storage serves as a strategic solution, yet optimizing its capacity depends on accurately modeling future renewable energy uncertainties to avoid over‐ or under‐investment. Existing approaches that use the historical extreme scenario set (HESS) to represent future conditions are contentious due to potential inadequacies in forecasting future extreme scenarios (ESs), including those on a decadal or centennial scale. This study addresses the issue by proposing an advanced energy storage expansion framework that leverages Extreme Value Theory (EVT) and a novel Deep Generative Model, namely the Diffusion Model. To model the extremes in a principled way, this work leverages EVT to establish a severity‐probability mapping for wind droughts, guiding the training process of the Diffusion Model. This model excels in generating ESs that accurately reflect the distribution of real‐world extremes, thereby significantly enhancing the predictive capacity of HESS. Case studies on a real‐world power system confirm the method's capacity to generate high‐quality ESs, encompassing the most severe historical wind droughts not included in the training dataset, thereby facilitating resilient energy storage expansion against unforeseen extremes.

Research on the capacity allocation of composite energy storage systems for hybrid ships

Abstract Aiming at the problem of unstable output power fluctuation of the dredger during operation, a composite energy storage system consisting of lithium iron phosphate battery and supercapacitor is introduced to smooth out the power fluctuation. Considering the configuration cost, operation and maintenance cost of the energy storage system, the capacity optimization model of the composite energy storage system is established with the average daily cost as the objective function, and with the system power balance, the charge state of the energy storage system, and the rated power as the constraints. The optimal capacity is solved by using the modified artificial fish swarm algorithm (MAFSA). The final results show that MAFSA reduces the capacity of the battery and the supercapacitor by 6.9% and 9.9%, respectively, and reduces the average daily cost of the system by 13.5%. Meanwhile, the capacity-optimized energy storage system enables the generator set to work in the optimal operating range, which reduces its output power fluctuation, improves its efficiency, and reduces fuel consumption and pollutant emission.

Power Transformer On-Load Capacity-Regulating Control and Optimization Based on Load Forecasting and Hesitant Fuzzy Control

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer.

Dynamic Improvement of a UPQC System Operating Under Grid Voltage Sag/Swell Disturbances

This paper proposes feed-forward control loops that accelerate the dynamic responses of both the dc-bus voltage and the compensated grid/input currents of a three-phase unified power quality conditioner (UPQC) under the occurrence of voltage sags/swells. The strategy adopted in this paper to control the UPQC is implemented by controlling the input currents and the output voltages for being in phase with their respective input voltages (grid voltages). Since the input currents and voltages are in phase, the UPQC series converter will furnish active power to the system during voltage sags. Thus, to maintain the system power balance, this active power is absorbed by the UPQC parallel converter. In contrast, under a voltage swell event, the series converter absorbs active power and furnishes it to the load through the parallel converter. Therefore, in both mentioned power quality events, the amplitudes of the UPQC input currents must be controlled appropriately to regulate the dc-bus voltage, prevent sudden dc-bus voltage transients, and perform the system power balance as fast as possible. Simulation and experimental results are achieved to present the effectiveness of the proposed feed-forward control loops.

A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.

Multi-Time-Scale Energy Storage Optimization Configuration for Power Balance in Distribution Systems

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years.

Multi-attribute decision-making method of pumped storage capacity planning considering wind power uncertainty

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness.

An End-to-End Network Evaluation Method for Differentiated Multi-Service Bearing in VPP

Virtual power plant (VPP) plays an important role in improving the balance and regulation abilities of new power system. The safe and reliable operation is support by the VPP end-to-end communication network with differentiated multi-service bearing capability. For the requirement of unified and standard VPP end-to-end networking scheme, the VPP service communication metrics, as well as the communication network architecture of VPP aggregation and control are analyzed. Then, a multi-dimension hierarchical VPP end-to-end network evaluation index system is put forward. In addition, an end-to-end VPP network evaluation method considering differentiated time-sensitive and granular requirements of multiple services is proposed. Finally, the suitability analysis results of various end-to-end networking schemes and multiple services with differentiated time-sensitive and granular requirements are given, which plays a guiding role in establishing a unified standard VPP end-to-end networking scheme.

Resilient Distributed Control Against False Data Injection Attacks for Demand Response

To maintain the power system balance, flexible load resources have been widely employed to provide operating reserves, which is named demand response (DR). The heating, ventilation, and air conditioning (HVAC) loads cover a high percentage of the total power consumption and have a significant regulation potential that deserves further investigation. To dispatch such dispersed HVACs, distributed control techniques are developed in the DR field due to their flexibility and scalability. However, the distributed DR system is vulnerable to potential cyber-attacks, i.e., false data injection (FDI) attacks, which may lead to DR failure. This paper proposes a resilient distributed controller to protect HVACs against FDI attacks. Firstly, an HVAC-based DR system is built by using distributed control to provide operating reserves for the power grid. Then, the adverse effect of FDI attacks is quantified with mathematical derivation. It is found that a small FDI attack can lead to severe power output deviations, which is a lethal problem for DR. On this basis, an FDI attack-resilient distributed controller is designed for HVACs so as to meet the grid's operating reserve requirements even under attacks. Moreover, the convergence of the proposed controller is proved based on the Laplace transform and final value theorem. Finally, case studies validate the effectiveness of the proposed resilient controller.

An Examination of the Koryŏ-Khitan Relations from the 10th to 12th Century through the Balance of Power System

International relations in East Asia from the 10th through the 12th centuries had been understood through a triangular relationship framework with the Khitan and the Song in a bipolar relationship and either Koryŏ or Western Xia as the third state. It is widely acknowledged that this framework has the advantage of simplifying international affairs under restricted conditions, which allows for an easy comprehension of the situation at the time. However, even the Song and the Khitan, which were considered to be major powers at the time, were merely two nations among equals. The period spanning the 10th to 12th centuries was not characterized by any single nation overpowering others. Consequently, the countries in East Asia during that time had to seek allies and strive for survival in response to the shifting international landscape. There are difficulties in understanding the international order of that era based solely on triangular relationships.The relationship between Koryŏ and the Khitan also did not lead to a situation where one country overpowered the other. Of course, there is a lot of room to believe that the Khitan has an overall advantage. However, as can be seen from the fact that the Khitans requested reinforcements from the Song to attack Koryŏ, the Khitans were also not confident that they were overpowering Koryŏ. Therefore, rather than confining the relationships among East Asian nations at the time within a rigid triangular framework, understanding the constantly shifting international situations at the time and the fact that all states prioritized their own security would bring us closer to the historical truth.

Day ahead demand response model with algorithm-based consumption classification and tariff planning

Demand Response (DR) programs effectively support the supply-demand balance in power systems. This study proposes a new Time-of-Use (ToU) tariff model, a type of price-based demand response (DR). The proposed model aims to shape the daily consumption profile of the distribution region by encouraging consumers through tariffs. Generally, in ToU demand response, various presumptions are being adopted to define consumption periods and consumer classes. It is unlikely that these presumptions are suitable for every day of the year and every consumer in the system. Achieving these definitions and classifications based on algorithms, free from human error, will increase the success and comprehensiveness of the DR program. To maximize success in this context, a deep learning model has been leveraged for day-ahead consumption forecasting. The forecasted daily consumption profile is partitioned into ToUs by using the Moving Boundary method. In addition, consumers within the region have been segmented based on the similarities in consumption behavior. Tariffs have been priced for each cluster through an optimization model that considers their participation rates in total consumption and contributions to consumption in ToU regions. The proposed model has been tested over a dataset belonging to a distribution system where different types of consumers coexist. Two sample days have been chosen as one is a weekday and the other is a weekend. The results prove that shifting consumption as encouraged by the proposed TOU method reduces consumers' costs. While, the profitability of the system operator is also preserved.