Energies

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18 pages, 9990 KiB

Open AccessArticle

Active Disturbance Rejection Control Design with Sensitivity Constraint for Drum Water Level

by Aimin Gao and Xiaobo Cui

Energies 2024, 17(6), 1438; https://doi.org/10.3390/en17061438 - 16 Mar 2024

Viewed by 1141

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum [...] Read more.

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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24 pages, 846 KiB

Open AccessArticle

Analysis of Variability in Electric Power Consumption: A Methodology for Setting Time-Differentiated Tariffs

by Javier E. Duarte, Javier Rosero-Garcia and Oscar Duarte

Energies 2024, 17(4), 842; https://doi.org/10.3390/en17040842 - 10 Feb 2024

Cited by 4 | Viewed by 1624

Abstract

The increasing concern for environmental conservation has spurred government initiatives towards energy efficiency. One of the key research areas in this regard is demand response, particularly focusing on differential pricing initiatives such as Time-of-Use (ToU). Differential tariffs are typically designed based on mathematical [...] Read more.

The increasing concern for environmental conservation has spurred government initiatives towards energy efficiency. One of the key research areas in this regard is demand response, particularly focusing on differential pricing initiatives such as Time-of-Use (ToU). Differential tariffs are typically designed based on mathematical or statistical models analyzing historical electricity price and consumption data. This study proposes a methodology for identifying time intervals suitable for implementing ToU energy tariffs, achieved by analyzing electric power demand variability to estimate demand flexibility potential. The methodology transforms consumption data into variation via the coefficient of variation and, then, employs k-means data analysis techniques and the a priori algorithm. Tested with real data from smart meters in the Colombian electrical system, the methodology successfully identified time intervals with potential for establishing ToU tariffs. Additionally, no direct relationship was found between external variables such as socioeconomic level, user type, climate, and consumption variability. Finally, it was observed that user behavior concerning consumption variability could be categorized into two types of days: weekdays and non-working days. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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18 pages, 1554 KiB

Open AccessArticle

A Power Grid Partitioning Method for Short-Circuit Current Considering Multi-Scenario Security with an Improved Direct Current Model

by Wentao Zhang, Ruipeng Guo, Yishan Shi, Yuchen Tang and Yi Lin

Energies 2023, 16(21), 7332; https://doi.org/10.3390/en16217332 - 29 Oct 2023

Viewed by 1248

Abstract

In order to ensure the security of power grids and control the level of short-circuit currents, a multi-objective optimization method for power grid partitioning is proposed. This method takes into consideration both short-circuit currents and multi-scenario safety constraints. A power grid partitioning optimization [...] Read more.

In order to ensure the security of power grids and control the level of short-circuit currents, a multi-objective optimization method for power grid partitioning is proposed. This method takes into consideration both short-circuit currents and multi-scenario safety constraints. A power grid partitioning optimization model is established to achieve objectives such as minimizing disconnected lines, maximizing safety margins, and ensuring load balance in the main transformers. The model aims to satisfy constraints related to short-circuit current levels, base-case power flow, and N − 1 security. To address the significant deviation in the static security constraint model caused by large amounts of active power losses in large-scale power grids, an improved direct current model is proposed to reduce these errors and meet the accuracy requirements for grid partitioning optimization. Additionally, to adapt to the variability of renewable energy output, an optimization method is proposed, combining three scenarios of renewable energy generation while satisfying short-circuit current and static security constraints. The power grid partitioning model is mathematically formulated as a large-scale mixed-integer linear programming problem, which presents challenges in terms of hardware requirements and computational complexity when solved directly. To mitigate these challenges, equivalent WARD values are assigned to the short-circuit current constraints, base-case constraints, and anticipated fault-induced power flow constraints. Anticipated faults and bottleneck branches are accurately incorporated, and the problem is decomposed into smaller-scale mixed-integer linear programming problems, solved in a stepwise iterative manner. This approach significantly improves computational efficiency and meets the requirements of practical large-scale power grid applications. To validate the proposed model and algorithm, a simulation program is developed using C++, and a simulation analysis of a regional transmission network is conducted. The program ensures the correctness of the proposed model and demonstrates the effectiveness of the algorithm. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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14 pages, 1019 KiB

Open AccessArticle

A Multi-Stage Real-Time Fast Search Method for Closed-Loop Paths Based on Grid Hierarchical Partitioning Characteristics

by Dongying Zhang, Kai Yang, Wei Wang, Yunbin Zhou, Xiong Hua, Tianjun Liang and Kunhao Song

Energies 2023, 16(18), 6561; https://doi.org/10.3390/en16186561 - 12 Sep 2023

Cited by 1 | Viewed by 1139

Abstract

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop [...] Read more.

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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19 pages, 6565 KiB

Open AccessArticle

Planning of an LVAC Distribution System with Centralized PV and Decentralized PV Integration for a Rural Village

by Dara Eam, Vannak Vai, Chhith Chhlonh and Samphors Eng

Energies 2023, 16(16), 5995; https://doi.org/10.3390/en16165995 - 16 Aug 2023

Cited by 1 | Viewed by 2925

Abstract

Energy demand is continuously increasing, leading to yearly expansions in low-voltage (LV) distribution systems integrated with PVs to deliver electricity to users with techno-economic considerations. This study proposes and compares different topology planning strategies with and without PVs in a rural area of [...] Read more.

Energy demand is continuously increasing, leading to yearly expansions in low-voltage (LV) distribution systems integrated with PVs to deliver electricity to users with techno-economic considerations. This study proposes and compares different topology planning strategies with and without PVs in a rural area of Cambodia over 30 years of planning. Firstly, the optimal radial topology from a distribution transformer to end-users is provided using the shortest path algorithm. Secondly, two different phase balancing concepts (i.e., pole balancing and load balancing) with different phase connection methods (i.e., power losses and energy losses) are proposed and compared to find the optimal topology. Then, the integration of centralized (CePV) and decentralized PV (DePV) into the optimal topology is investigated for three different scenarios, which are zero-injection (MV and LV levels), no sell-back price, and a sell-back price. Next, the minimum sell-back price from CePV and DePV integration is determined. To optimize phase balancing, including the location and size of PV, an optimization technique using a water cycle algorithm (WCA) is applied. Finally, an economic analysis of each scenario based on the highest net present cost (NPC), including capital expenditure (CAPEX) and operational expenditure (OPEX) over the planning period, is evaluated. In addition, technical indicators, such as autonomous time and energy, and environmental indicator, which is quantified by CO₂ emissions, are taken into account. Simulation results validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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15 pages, 6575 KiB

Open AccessArticle

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

by Baoqun Zhang, Cheng Gong, Yan Wang, Longfei Ma, Dongying Zhang and Shiwei Xia

Energies 2023, 16(14), 5259; https://doi.org/10.3390/en16145259 - 9 Jul 2023

Cited by 1 | Viewed by 1466

Abstract

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic [...] Read more.

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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22 pages, 4745 KiB

Open AccessArticle

Decomposition-Based Multi-Classifier-Assisted Evolutionary Algorithm for Bi-Objective Optimal Wind Farm Energy Capture

by Hongbin Zhu, Xiang Gao, Lei Zhao and Xiaoshun Zhang

Energies 2023, 16(9), 3718; https://doi.org/10.3390/en16093718 - 26 Apr 2023

Viewed by 1459

Abstract

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution [...] Read more.

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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17 pages, 3482 KiB

Open AccessArticle

Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control

by Abebe Tilahun Tadie, Zhizhong Guo and Ying Xu

Energies 2022, 15(23), 9244; https://doi.org/10.3390/en15239244 - 6 Dec 2022

Cited by 7 | Viewed by 2419

Abstract

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion [...] Read more.

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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15 pages, 2361 KiB

Open AccessArticle

Power System Fault Diagnosis Method Based on Deep Reinforcement Learning

by Zirui Wang, Ziqi Zhang, Xu Zhang, Mingxuan Du, Huiting Zhang and Bowen Liu

Energies 2022, 15(20), 7639; https://doi.org/10.3390/en15207639 - 16 Oct 2022

Cited by 7 | Viewed by 2456

Abstract

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, [...] Read more.

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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18 pages, 3378 KiB

Open AccessArticle

Transmission Expansion Planning Considering Wind Power and Load Uncertainties

by Yilin Xie and Ying Xu

Energies 2022, 15(19), 7140; https://doi.org/10.3390/en15197140 - 28 Sep 2022

Cited by 14 | Viewed by 1979

Abstract

Due to the rapidly increasing power demand worldwide, the development of power systems occupies a significant position in modern society. Furthermore, a high proportion of renewable energy resources (RESs) is an inevitable trend in further power system planning, due to traditional energy shortages [...] Read more.

Due to the rapidly increasing power demand worldwide, the development of power systems occupies a significant position in modern society. Furthermore, a high proportion of renewable energy resources (RESs) is an inevitable trend in further power system planning, due to traditional energy shortages and environmental pollution problems. However, as RESs are variable, intermittent, and uncontrollable, more challenges will be introduced in transmission expansion planning (TEP). Therefore, in order to guarantee the security and reliability of the power system, research related to TEP with the integration of RESs is of great significance. In this paper, to solve the TEP problem considering load and wind power uncertainties, an AC TEP model solved by a mixed integer non-linear programming (MINLP) is proposed, the high-quality optimal solutions of which demonstrate the accuracy and efficiency of the method. Latin hypercube sampling (LHS) is employed for the scenario generation, while a simultaneous backward reduction algorithm is applied for the scenario reduction, thus reducing the computational burden. Through this method, the reserved scenarios can effectively reflect the overall trends of the original distributions. Based on a novel worst-case scenario analysis method, the obtained optimal solutions are shown to be more robust and effective. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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18 pages, 12651 KiB

Open AccessArticle

A Distributed Frequency Regulation Method for Multi-Area Power System Considering Optimization of Communication Structure

by Yicong Wang, Chang Liu, Ji Han, Haoyu Tan, Fangchao Ke, Dongyin Zhang, Cong Wei and Shihong Miao

Energies 2022, 15(18), 6582; https://doi.org/10.3390/en15186582 - 8 Sep 2022

Viewed by 1725

Abstract

Nowadays, the influences of the communication structure on the frequency regulation performance in a multi-area power system are barely studied. In this paper, a decentralized frequency regulation method for a multi-area power system considering optimization of communication structure is presented, and the influence [...] Read more.

Nowadays, the influences of the communication structure on the frequency regulation performance in a multi-area power system are barely studied. In this paper, a decentralized frequency regulation method for a multi-area power system considering optimization of communication structure is presented, and the influence of the communication structure on the frequency regulation performance is studied. Firstly, the communication network model is described and the multi-area power system model considering communication structure is presented. Then, the optimization model of communication structure during a decentralized frequency regulation process is constructed. This model aims to speed up the convergence speed of the control together with ensuring the high algebraic connectivity of the communication structure. Quantum binary particle swarm optimization (QB-PSO) algorithm is introduced to solve this model and, based on this, the communication structure optimization process and frequency regulation method are proposed. The simulation results show that the proposed method could greatly improve the frequency control efficiency through the optimization of the communication structure. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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14 pages, 1555 KiB

Open AccessArticle

Continuous Line Loss Calculation Method for Distribution Network Considering Collected Data of Different Densities

by Yuying Li, Xiping Ma, Chen Liang, Yaxin Li, Zhou Cai and Tong Jiang

Energies 2022, 15(14), 5171; https://doi.org/10.3390/en15145171 - 16 Jul 2022

Cited by 4 | Viewed by 2026

Abstract

In order to find the causes of statistical line loss abnormalities and propose better loss reduction strategies, it is necessary to improve the accuracy of theoretical line loss calculations. Since Distributed Generation (DG) access to the distribution network causes variability of power flow [...] Read more.

In order to find the causes of statistical line loss abnormalities and propose better loss reduction strategies, it is necessary to improve the accuracy of theoretical line loss calculations. Since Distributed Generation (DG) access to the distribution network causes variability of power flow in the distribution network within a short period, the error of the traditional line loss calculation method increases. For the line loss calculation of medium-voltage distribution networks containing DGs with high-density collection data, a continuous line loss calculation method for the distribution network was proposed, aiming at improving the accuracy compared with the traditional line loss calculation method. The proposed method makes full use of the high-density collection data on the dispatch side and DG side, distributing the supply power to each load node by power to calculate real-time power flow, thus obtaining a more credible line loss value. The effectiveness and accuracy of the proposed method were verified in the IEEE 17-node distribution system. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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15 pages, 1721 KiB

Open AccessArticle

Evaluation of Acceptance Capacity of Distributed Generation in Distribution Network Considering Carbon Emission

by Yixin Huang, Lei Zhao, Weiqiang Qiu, Yuhang Xu, Junyan Gao, Youxiang Yan, Tong Wu and Zhenzhi Lin

Energies 2022, 15(12), 4406; https://doi.org/10.3390/en15124406 - 16 Jun 2022

Cited by 5 | Viewed by 1932

Abstract

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of [...] Read more.

Under the background of renewable-dominated electric power system construction, the penetration rate of low-carbon and renewable distributed generation (DG) in distribution network is increasing, which has changed the form and operation mode of the distribution network. To deal with the output fluctuation of high penetration DG in the distribution network operations, it is necessary to evaluate the acceptance capacity of DG. The correct evaluation can realize the secure, economic and low-carbon configuration of DG. In this paper, an evaluation method of acceptance capacity of DG in the distribution network considering the carbon emission is proposed. Firstly, a multi-objective evaluation model of acceptance capacity of DG is constructed with the objectives of minimizing carbon emission in the full life cycle, minimizing node voltage deviation and maximizing line capacity margin. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is employed to solve the model to determine the Pareto optimal solutions of DG configuration. Then, the comprehensive index of acceptance capacity evaluation is obtained based on entropy weight method to decide the optimal compromise solution. Finally, an actual 55-bus distribution network in China is used to verify the effectiveness of the proposed method. The simulation results show that the proposed evaluation method can comprehensively obtain the optimal compromise solution considering the reliability, economy and carbon emission benefits of distribution network operation, which guides the DG configuration in the distribution network. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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16 pages, 2042 KiB

Open AccessArticle

Line Loss Interval Algorithm for Distribution Network with DG Based on Linear Optimization under Abnormal or Missing Measurement Data

by Chen Liang, Chang Chen, Weizhou Wang, Xiping Ma, Yuying Li and Tong Jiang

Energies 2022, 15(11), 4158; https://doi.org/10.3390/en15114158 - 6 Jun 2022

Cited by 7 | Viewed by 2074

Abstract

Data collection is more difficult in distribution network than transmission networks since the structure of distribution networks is more complex. As a result, data could be partly abnormal or missing, which cannot completely describe the operation status of distribution network. In addition, access [...] Read more.

Data collection is more difficult in distribution network than transmission networks since the structure of distribution networks is more complex. As a result, data could be partly abnormal or missing, which cannot completely describe the operation status of distribution network. In addition, access of distributed generation (DG) to distribution network further aggravates the variability of power flow in distribution network. The traditional deterministic line loss calculation method has some limitations in accurately estimating the line loss of distribution network with DG. A line loss interval calculation method based on power flow calculation and linear optimization is proposed, considering abnormal data collection and distribution network power flow variability. The linear optimization model is established according to sensitivity of line loss to the injected power and sensitivity of transmission power of first branch to the injected power. Introducing the scheduling information into the optimization model, a reliable line loss fluctuation interval can be obtained which actual line loss locates. The effectiveness of the proposed algorithm is verified in IEEE 33-bus distribution network system. Full article

(This article belongs to the Special Issue Optimal Operation and Control of Energy System and Power System)

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