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Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volatile electricity prices have raised concerns about the economic feasibility of wind projects in Finland. This study assesses the economic viability and optimal operational strategies for integrating wind-powered green hydrogen production systems. Utilizing modeling and optimization, this research evaluates various wind farms in Western Finland over electricity market scenarios from 2019 to 2022, with forecasts extending to 2030. Key economic metrics considered include internal rate of return, future value, net present value (NPV), and the levelized cost of hydrogen (LCOH). Results indicate that integration of hydrogen production with wind farms shows economic benefits over standalone wind projects, potentially reducing LCOH to €2.0/kgH 2 by 2030 in regular and low electricity price scenarios, and to as low as €0.6/kgH 2 in high-price scenarios. The wind farm with the highest capacity factor achieves 47% reductions in LCOH and 22% increases in NPV, underscoring the importance of strategic site selection and operational flexibility. • Volatility of electricity prices raises viability concerns for wind producers. • Examining the optimal operation and economic feasibility of wind-hydrogen systems. • Evaluated wind farms in Western Finland from 2019 to 2022, with a 2030 outlook. • Integration with hydrogen production enhances financial resilience and adaptability. • Strategic site selection and adaptive PEMEC operational plans found crucial. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are massive infrastructure systems designed to supply water from sources to consumers. The optimal operation problem of WDSs is the problem of determining pump and tank operation to meet the consumers' demands with minimal operating cost, under different constraints, which often include hydraulic feasibility, pressure boundaries, and water quality standards. The water quality aspect of WDSs' operation poses significant challenges due to its complex mathematical nature. Determined by mixing in the systems' nodes, it is affected by flow directions, which are subject to change based on the hydraulic state of the system and are therefore difficult to either predict, control, or be included in an analytical model used for optimization. Water age, which is defined as the time water travels in the system until reaching the consumer, is often used as a general water quality indicator—high values of water age imply low water quality, whereas low values of water age usually mean fresher, cleaner, and safer water. In this work, we present the effects that tank operation has on water age. As tanks contain large amounts of water for long periods of time, water tends to age there significantly, which translates into older water being supplied to consumers. By constraining the tank operation, we aim to present the trade-off between water age, tank operation, and operational cost in the WDS optimal operation problem and provide an operational tool that could assist system operators to decide how to operate their system, based on their budget and desired water age boundary. The analysis is applied to three case studies that vary in size and complexity, using MATLAB version R2021b and EPANET 2.2. The presented results show an ability to mitigate high water age in water networks through tank constraints, which varies in accordance with the system's complexity and tank dominance in supply. The importance of a visual tool that serves as a guide for operators to tackle the complex problem of controlling water age is demonstrated as well. [ABSTRACT FROM AUTHOR]

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are designed to convey water from sources to consumers. Their operation is a main concern for engineers, researchers, and practitioners and is subject to demand, pressure, and quality constraints. Pumping stations require power to pump water and keep system pressure at a desired level. On the other hand, power is generally supplied through power grids (PGs), which require optimal operation while satisfying operational constraints, such as generation limits, power consumption, and voltage constraints. Since the two infrastructure systems are interconnected, decision-makers could benefit from a holistic approach that would allow solving the two operational optimization problems together as one conjunctive problem. This paper presents the full mathematical formulation of the conjunctive optimization problem, including a novel modelling approach for the operation of a variable speed pump, which does not include integer variables for pump status, thus allowing to solve the model as a non-linear programming (NLP) problem. The formulation is applied to two illustrative case studies, and the results are compared to the optimal operation of the independent WDS. The inclusion of the PG in the optimization problem is observed clearly in the results and influences them quite significantly. WDS operation is shown adjust to the PG constraints, and the application of the conjunctive model results in a cost reduction rate of more than 10 % for both case studies.

Commercial virtual power plant is one of the most efficient means to solve the problem of multi-type distributed power supply and flexible load scheduling. Aiming at the problems of uncertain renewable energy sources and reliable distribution system, a flexible interactive coordinated control method for commercial virtual power plants based on worst-case conditional value-at-risk (WCVAR) is proposed in this paper. In order to reduce the risk of voltage exceeding limits and increased network losses at distribution network nodes, combined with the requirements of technology virtual power plants for power flow control technology in distribution networks, a commercial virtual power plant net profit model based on the reliability of distribution network operation is established. The worst-case conditional risk value theory is used to quantify the risks caused by the uncertainty of wind, water and photovoltaic outputs. A commercial virtual power plant optimization scheduling model based on the WCVAR is established with the goal of minimizing risk cost, and the model is solved by commercial software Cplex. By simulating and analyzing the actual operating data of the improved IEEE-33 distribution system, the output characteristics of commercial virtual power plants under different risk confidence levels are compared and analyzed. The simulation results show that this method fully explores the scheduling potential between electricity, power grid, load and energy storage in commercial virtual power plants, flexibly adjusts risk preferences according to scheduling needs, and achieves flexible control of benefits and risks in commercial virtual power plants.

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water is one of the most important resources needed by human society and the first and most important factor for the production of agricultural products, more than 90% of this vital liquid is consumed in this sector. One of the most important factors that affect the performance of a water conveyance and distribution network is the water distribution and delivery program. In order to obtain turnouts’ discharges, the water requirement of the eastern Aghili area was estimated using the Global Land Data Assimilation System (GLDAS) and controlled using the results of the NETWAT model. For this purpose, three-hour evapotranspiration was estimated with GLDAS, and the six-hour discharges of turnouts were calculated according to the cultivated area of each turnout and irrigation efficiency. The hydraulics of the eastern Aghili canal were simulated using the above-mentioned data for six hours. The results showed the appropriate accuracy of GLDAS so that at a maximum of 12.7%, GLDAS underestimated the evapotranspiration values compared to NETWAT. The minimum values of efficiency and adequacy indicators of 0.95 and 0.94, respectively, were obtained, which are in the "good" performance class. [ABSTRACT FROM AUTHOR]

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources. This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and "cradle-to-gate" as the system boundary. Based on the baseline scenario, a natural gas generator set was added to replace central heating, and the light pipes were expanded to constitute the optimized scenario. The results showed that the key impact categories for both scenarios were global warming, fine particulate matter formation, human carcinogenic toxicity, and human non-carcinogenic toxicity. The overall environmental impact of the optimized scenario was reduced by 68% compared to the baseline scenario. A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios, followed by central heating and natural gas. Therefore, to reduce the environmental impact of network systems, it is necessary to further optimize the grid power structure. The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the large proportion of China's energy consumption used by industry, in response to the national strategic goal of "carbon peak and carbon neutrality" put forward by the Chinese government, it is urgent to improve energy efficiency in the industrial field. This paper focuses on the optimization of an integrated energy system with supply–demand coordination in an industrial park. This optimization is formulated as a "node-flow" model. Within the model, each node is designed according to the objective function of its own operation and the energy coupling relationship. The flow model is designed based on the energy flow interaction relationship between each node. Based on the "node-flow" model, an edge–cloud information interaction mechanism based on energy transfer balance between nodes is proposed to describe the way the system interacts with information, and a distributed iterative optimization algorithm based on edge–cloud collaboration is designed to realize the optimization decision of each node. The performance of the method proposed in this paper is demonstrated using a practical case study of an industrial park integrated energy system in Xinjiang. The results show that the proposed model can effectively improve the utilization efficiency of multi-energy synergy and complementation in the industrial park, and the proposed algorithm can shorten the solution time by more than 50% without significantly affecting the accuracy of the solution. [ABSTRACT FROM AUTHOR]

The Wastewater Treatment Plant (WWTP) in the paper industry faces challenges in controlling and estimating Chemical Oxygen Demand (COD) to improve monitoring and optimize the process under varying operational and environmental conditions. However, maintaining the COD content at the desired level becomes difficult due to constantly changing environmental conditions and stricter regulations. A common method in WWTP to remove organic COD is aeration treatment. For simulating the aeration process, Activated Sludge Model No.1 (ASM1) is a widely used tool, but it requires an abundance of influent COD concentration samples to estimate the effluent COD. Collecting these samples is costly both in terms of time and human resources. This study aims to use limited wastewater samples to generate artificial data for the ASM1 model using linear regression techniques. The objective is to reduce costs associated with COD concentration sample collections for a WWTP processing wastewater of a Finnish paper mill, while still providing reliable estimations for effluent COD over an extended period by identifying the optimal tuning parameters for the ASM1.

Electronic power converters are extensively applied in microgrids to interface distributed energy resources to the grid. Besides the primary active-power control, electronic converters support increasingly advanced and flexible control capabilities. While the former is typically bound to local needs (e.g., maximum power extraction from renewables), additional degrees of freedom can be exploited to support microgrid operation. This article proposes a control algorithm that allows the optimal provision of reactive power, negative-sequence, and zero-sequence currents by distributed converters. The resulting operation shows enhanced power quality at the point of common coupling (PCC) and limited conversion losses, which are taken into account in the optimization algorithm. Three modes of operation are discussed, that is, i) power loss minimization; ii) balanced currents at the PCC; iii) zero reactive power flow at the PCC. An algorithm based on the primal-dual method is proposed to solve the optimization problem. Results based on experimental measurements are discussed to prove the effectiveness of the proposal.

Energy hubs (EHs) enable all types of energy customers to participate in demand response programs (DRPs), such as inelastic loads, by combining electricity, heat, natural gas, and other types of energy. Integrated demand response (IDR) is the result of this new vision. From a global warming perspective, environmental emissions are a significant issue to be considered. Furthermore, hydrogen has been recognized as an attractive fuel for decarbonizing sectors that contribute to global warming. Thus, this paper provides a solution to global environmental problems through the utilization of renewable energy sources (RESs) and green hydrogen. In addition, electric vehicles (EVs) are expected to contribute significantly to this scenario due to their rapid expansion. Considering the uncertainty of electricity prices, this paper focuses on coordinating EV parking with hydrogen storage systems (HSS) and IDR with the aim of increasing flexibility, where a robust optimization (RO) method has been implemented to solve the problem. The results demonstrate that in the case of a deterministic solution to the problem and where uncertainty is at the highest level, the proposed scheme reduces the total operating costs by 13.89% and 8.67%, respectively. This indicates that the proposed scheme could avoid overinvestment and cost-effectively achieve the given carbon emission target.

An energy router can effectively optimize the network loss and power quality of distribution networks. The optimal configuration of the energy router is relatively complex because issues dynamically influence each other, such as the location of the energy router, optimal number and capacity of its ports, and building new distribution lines for the ports. Presently, there has been very few research studies on the practical problems for the energy router. In this paper, a planning model of an energy router combined with the distribution network is established, which fully exploits the active and reactive power control abilities of the energy router to optimize the operation of the grid. The configuration problem is decoupled into two stages. The upper layer determines the location of the energy router and the parameters of the candidate new distribution lines for the ports. The lower layer calculates the optimal configuration of the energy router by minimizing the total annual cost. Because of the same rated bus voltage in the distribution network, the existing structure of the energy router is changed to be more applicable for the distribution network, and then the power flow model of the energy router is also modified. Due to the nonlinearity of the model, the planning model is converted into a mixed-integer second-order cone model to solve efficiently. In addition, some core factors influencing the optimal scheme of the energy router are also analyzed in this paper. Simulation results show that the optimal scheme of the energy router can significantly improve the economic deployment of the energy router, and optimize the network loss and power quality of the distribution network.

As renewable energy continues to be integrated into the grid, energy storage has become a vital technique supporting power system development. To effectively promote the efficiency and economics of energy storage, centralized shared energy storage (SES) station with multiple energy storage batteries is developed to enable energy trading among a group of entities. In this paper, we propose the optimal operation with dynamic partitioning strategy for the centralized SES station, considering the day-ahead demands of large-scale renewable energy power plants. We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory. This model is decomposed into two subproblems: the operation profit maximization problem with energy trading and the leasing payment bargaining problem. The distributed alternating direction multiplier method (ADMM) is employed to address the subproblems separately. Simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities, enhances the actual utilization rate of energy storage, and increases the profits of each participating entity. The results confirm the practicality and effectiveness of the strategy.

Abstract This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation optimization model of PIES integrating flexible load and carbon trading mechanism is constructed. Firstly, according to the characteristics of load response, the demand response is divided into four types: which can be shifted, transferred, reduced and replaced. Secondly, the PIES basic architecture is given by considering the combined heat and power generation coupling equipment, new energy and flexible load in the park. Finally, introducing the ladder-type carbon trading mechanism into the system and minimize the total operating cost, the low-carbon economic operation optimization model of PIES is established. The YALMIP toolbox and CPLEX solver are used to solve the example, the simulation results show that the participation of electrical and thermal coupled scheduling and flexible electric or thermal loads can significantly reduce the system operating cost, reduce the load peak-to-valley difference and relieve peak power consumption pressure.

The rapid growth and development in DC microgrids (DCmGs), led system-level operation and control to be reformed continuously imbibing constraints and concerns that improve system operational performance. Focusing only on islanded DCmGs, quite a literature/work has been attempted in various perspectives to solve energy management problems. In light of this aspect, an optimal operation/control of standalone DCmGs, composed of the droop-regulated dispatchable type distributed generators (DGs) considering generation cost, voltage deviation, and current-sharing objectives has been proposed. The proposed work includes the uncertainties in network variables such as load demand, wind, and solar power generations. The scenario-based analytical method has been employed for modeling the uncertainties stated. For optimal operation, the droop parameters were solved by means of the heuristic-based multi-objective optimization technique, Dragonfly Algorithm (DA). The proposed methodology has been implemented on modified 6-bus and 33-bus test systems operated as DCmGs. Furthermore, the applicability of the proposed approach has been presented for the case study with battery energy storage system (BESS) scheduled DCmG network operation on a 24-hour time horizon. The obtained results have been compared with other optimization algorithms to validate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving the stochastic optimal operation problem (OOP) in the IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, and pricing. The proposed I-WaOA utilizes three strategies: the fitness-distance balance method, quasi-opposite-based learning, and Cauchy mutation. The I-WaOA optimally locates and sizes photovoltaic (PV) ratings and wind turbine (WT) capacities and determines the optimal power factor of WT with DSR. Using Monte Carlo simulations (MCS) and probability density functions (PDF), the uncertainties in renewable energy generation, load demand, and energy costs are represented. The results show that the proposed I-WaOA approach can significantly reduce costs, improve voltage stability, and mitigate voltage deviations. The total annual costs are reduced by 91%, from 3.8377 × 107 USD to 3.4737 × 106 USD. Voltage deviations are decreased by 63%, from 98.6633 per unit (p.u.) to 36.0990 p.u., and the system stability index is increased by 11%, from 2.444 × 103 p.u. to 2.7245 × 103 p.u., when contrasted with traditional methods. [ABSTRACT FROM AUTHOR]

The increasing integration of electricity and heating networks escalates the operation risk of an integrated energy system (IES). The vulnerable components in IES, which play an important role of aggravating the spread of IES failures, is one of the most important parts of risk control for IES operation. To ensure the operation security and stability of IES, an operation optimization method considering the vulnerability prevention for an electricity–heat IES is proposed in this paper. The method contains two main stages: vulnerability identification and optimal operation. First, the IES cascading failure space-time graph (CFSTG) is formed by simulating the cascading failure development stages in IES, which can effectively measure the impact of vulnerable branches on the cascading failure depth and breadth. Then, the vulnerable branches of IES are initially identified based on indices of node degree calculated according to CFSTG. In order to further screen and correct the initial identification results, a prevention–correction hybrid control strategy is proposed. On this basis, a day-ahead optimal operation bilevel model is established. Overall IES operating cost and static security are taken into account in the outer layer of the model to optimize electricity and heat output of each energy hub. After obtaining a multiobjective optimal energy flow distribution of IES, the inner layer of the model is developed to optimize the output of each unit in the energy hubs. Finally, an IES test system is utilized as an example to verify the effectiveness of the proposal method. [ABSTRACT FROM AUTHOR]

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case. [ABSTRACT FROM AUTHOR]

In this study, the performance of the Differential Evolution (DE) Algorithm, the Genetic Algorithm (GA) and Teaching-Learning Based Optimization (TLBO) Algorithm are compared for the optimum operation of the Mahabad dam reservoir. The Reservoir supplies agricultural, environmental, municipal and industrial water demands of the area. The desktop reserve model is used for estimating the minimum environmental demand. Six alternative agricultural water management scenarios are proposed and a sensitivity analysis is performed on the agricultural demands under different scenarios. The results of the study indicate that both DE and GA algorithms performed nearly equally; however, the DE Algorithm reached its result more quickly with the reliability of 74.19 % compared to the results of the GA and TLBO. A 30 % decrease in agricultural demand in the fifth scenario with 87.56 % reliability provides the best results.

With the increasing scale of Electric Vehicles (EVs) connected to the grid, in order to achieve a win–win situation between the grid and Electric Vehicles, it is necessary to consider not only the impact of disordered charging and discharging on the grid load, but also the costs of both sides. This paper establishes an optimal operation model for Electric Vehicles in Electric Power Aggregators (EPAs) based on Stackelberg Game Model. EPA formulates appropriate charge and discharge prices and optimizes scheduling strategies as the upper leader. The lower follower EVs responds optimally to minimize costs. The IEEE33 node system is used to analyze the example, simulation results show that the model not only minimizes the cost of both sides, but also reduces the load power fluctuation due to the large number of Electric Vehicles connected to the grid, and reduces the peak–valley load difference.

Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm Optimization algorithm (PSO) and the Moth-flame Optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability.

In the context of carbon peak and carbon neutrality target of China, multi-energy systems have become a key way to meet the needs of a low-carbon economy in the energy sector, and can achieve optimization and integration of multi-energy sources. For the purpose of maximize the low-carbon and economic of multi-energy systems, this paper proposes an optimization operation strategy of electricity–heat–gas integrated energy systems (EHGIES) based on bi-directional tiered-pricing carbon trading. The paper first establishes a multi-energy system model with CHP system and P2G conversion, introduces a bi-directional tiered-pricing carbon trading mechanism. Subsequently, an optimization model with objective function of the lowest carbon trading cost and energy purchase cost is established. The nonlinear optimization model is converted to a linear model by the CPLEX solver. Finally, this paper analyzes the trading mechanism, the interval length of tiered-pricing and carbon trading price on carbon emissions and carbon trading cost of EHGIES. The results show that different carbon trading price ranges and price growth rates have different impacts on carbon emissions and carbon trading costs. At the same time, the bi-directional tiered-pricing carbon trading mechanism established can reduce the overall carbon emissions effectively and is conducive to the low-carbon operation, and the low-carbon and economic nature of system operation can effectively be taken into account.

Water distribution systems (WDSs) are massive infrastructure systems designed to supply water from sources to consumers. The optimal operation problem of WDSs is the problem of determining pump and tank operation to meet the consumers’ demands with minimal operating cost, under different constraints, which often include hydraulic feasibility, pressure boundaries, and water quality standards. The water quality aspect of WDSs’ operation poses significant challenges due to its complex mathematical nature. Determined by mixing in the systems’ nodes, it is affected by flow directions, which are subject to change based on the hydraulic state of the system and are therefore difficult to either predict, control, or be included in an analytical model used for optimization. Water age, which is defined as the time water travels in the system until reaching the consumer, is often used as a general water quality indicator—high values of water age imply low water quality, whereas low values of water age usually mean fresher, cleaner, and safer water. In this work, we present the effects that tank operation has on water age. As tanks contain large amounts of water for long periods of time, water tends to age there significantly, which translates into older water being supplied to consumers. By constraining the tank operation, we aim to present the trade-off between water age, tank operation, and operational cost in the WDS optimal operation problem and provide an operational tool that could assist system operators to decide how to operate their system, based on their budget and desired water age boundary. The analysis is applied to three case studies that vary in size and complexity, using MATLAB version R2021b and EPANET 2.2. The presented results show an ability to mitigate high water age in water networks through tank constraints, which varies in accordance with the system’s complexity and tank dominance in supply. The importance of a visual tool that serves as a guide for operators to tackle the complex problem of controlling water age is demonstrated as well.

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Storage systems are needed to boost the reliability of intermittent solar and wind resources in power networks. Rather than focus on one storage system or one hybrid energy storage system (HESS), this work models the operation of six HESS configurations in a Renewable Energy (RE) based grid-tied network. The objective is to minimise the daily operational costs of the microgrid while prolonging the storage lifetime by considering storage degradation costs. The influence of fixed tariffs and time-of-use (TOU) tariffs on the optimal operational of the HESS configurations have also been investigated; as well as deferrable demand satisfaction, charge-discharge pattern of different HESS and availability of the power-dense storage system within the microgrid. Results show that the lead-acid battery and hydrogen fuel cell (HFC) HESS incurs the highest operational costs, while the supercapacitor-lead-acid battery HESS incurs the lowest operational costs. The supercapacitor-lead acid battery and the supercapacitor-HFC HESS incur the highest annual storage degradation costs. The grid expenses were seen to be the same for all HESS under each tariff scheme. Lastly, decreasing the minimum storage level further by 10% from the 30% in the base case, led to an increase in the number of hours of availability of the power-dense storage system of five of the six HESS. These results have given a deeper understanding to the operation of HESS systems and can inform better decision making of the suitable HESS to be deployed in different operating scenarios. [ABSTRACT FROM AUTHOR]

Huangzangsi Reservoir is the first comprehensive utilization reservoir in the upper reaches of the Heihe River. Supported by the new energy policy of Qinghai Province, if the Huangzangsi Reservoir is equipped with photovoltaic power stations, a complex complementary system of hydropower and photovoltaic power generation in the upper reaches of the Heihe River will be formed. This study used PVsyst software to complete the design of a 100 MW photovoltaic plant, and proposed a daily output clustering method based on the number of output curve fluctuations, maximum output change and output proportion as indicators. A daily optimal operation model for Huangzangsi Reservoir was established with the goal of stabilizing the output of the Hydro-PV complementary system and improving the power generation revenue of cascade hydropower stations and solved by using the NSGA-Ⅱ. The results indicate that cloudy weather, peak shaving power generation by cascade hydropower stations, installed capacity of Huangzangsi Reservoir and water volume limitations that can be regulated are the main factors affecting the stability of the output of the Hydro-PV complementary system; When the Huangzangsi Reservoir compensates for photovoltaic output, the outgoing flow has a small fluctuation, this fluctuation is absorbed by the Baopinghe Hydropower Station; When the Huangzangsi Reservoir compensates for the cascade hydropower station peaking and power generation, the upstream produces a large flow of incoming water, Longshou-Ⅱ Hydropower Station ensures that the Yingluoxia section has a stable flow throughout the day; The Hydro-PV complementary mode has a small impact on the hydropower benefits of Huangzangsi Reservoir and has a significant effect on stabilizing the fluctuation of photovoltaic output, however, the capacity configuration of photovoltaic power stations should be further optimized. [ABSTRACT FROM AUTHOR]

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan'an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This editorial provides a summary of the topics and papers selected for the Special Issue on "optimal operation and control of smart energy systems". After undergoing a rigorous review process, 10 papers have been accepted for publication. These papers cover various aspects of smart energy systems and are distributed as follows: control strategy of distributed resources (two papers), optimal operation of energy systems under uncertainties (three papers), defect detection and risk evaluation of energy systems (two papers), low‐carbon economical energy systems (three papers). The Guest Editorial Board hopes this Special Issue can provide valuable information for future research and advancements in the field of optimal operation and control of smart energy systems. This editorial summarizes the topics and papers selected for the Special Issue on "optimal operation and control of smart energy systems". After rigorous reviewing process, 10 papers are accepted for publication. The Guest Editorial Board hopes this Special Issue can provide a valuable information for future research and advancements in the field of optimal operation and control of smart energy systems. [ABSTRACT FROM AUTHOR]

Cascade reservoir operation is an effective nonstructural countermeasure for water resources management. In recent years, many metaheuristic algorithms are introduced to handle reservoir optimal operation due to their strong search capability and high efficiency. The butterfly optimization algorithm (BOA) is a newly developed metaheuristic method which has been widely used in solving various optimization problems. But it has local convergence and premature problems. Therefore, this paper proposed an improved version of BOA where three strategies are introduced: (1) the self-adaptive strategy to improve the initial population, (2) the dynamic switch strategy to balance exploration and exploitation, (3) the Levy-flight and standardized fragrance operators for position updating. The feasibility of the BOA, and IBOA are verified and compared with several commonly used algorithms (PSO, SCA, WOA and TSA) based on 19 test functions. Then, these methods are applied to address the optimization of cascade reservoirs that aims to maximize total hydropower generation. The results show that the proposed IBOA produces higher hydropower output and more stable results, indicating better scheduling schemes than BOA and the other four algorithms. In conclusion, IBOA is an effective and robust alternative optimization tool for cascade reservoir operation problems. HIGHLIGHTS An improved butterfly optimization algorithm (IBOA) was developed.; Dynamic switching strategy was used to balance exploration and exploitation.; The IBOA has evaluated over 19 benchmark functions and a reservoir operation problem.; IBOA superiority over several advanced algorithms was shown.;