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In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno–economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. The study aims to maximize the technical, financial, and social benefits while ensuring that all security constraints are met. To assess the financial viability of the proposed model over a 10-year horizon, a detailed economic analysis comprising installation cost, operation, and maintenance cost is conducted. To make the model more realistic, various practical parameters, such as the inflation rate and interest rate, are incorporated during the financial analysis. Additionally, to highlight the societal benefits of the approach, the study quantifies the long-term carbon emissions and the corresponding cost of emissions. The proposed framework is tested on both a 33-bus distribution network and a 108-bus Indian distribution network. Various planning scenarios are explored, with different configurations of the EVCE, solar-based DG, and DSTATCOM, to assist power system planners in selecting the most suitable strategy. [ABSTRACT FROM AUTHOR]

Some applied problems of the theory of controlled random processes arising in the reliability and queueing theories are analyzed. The conditions whereby finding the optimal strategy is reduced to some mathematical programming problems and numerical methods of their solution are given. [ABSTRACT FROM AUTHOR]

Within the wider discourse on economic planning, this paper critically interrogates practical challenges in applying optimisation algorithms to achieve allocative efficiency on the scale of national economies. In questioning whether contemporary information technology solves the shortcomings of Soviet command planning, the paper provides a historical introduction to optimal planning and engages with four fundamental problems optimal planners would face, namely: the concern of computational complexity, the challenge of generating the economic data necessary for the constitution of such a system, the issue of reconciling a static optimum with the dynamism of real economies, and ultimately the relation of optimisation solvers to a hierarchy of ends. Besides epistemic concerns, the greatest risk of such a planning framework is constituted by the possibility of a slow descent into an authoritative system that would mirror the Soviet experience because of its reliance on bureaucratic institutions to make this machine work. [ABSTRACT FROM AUTHOR]

This paper presents the formulation of the simultaneous planning of distributed generations (DGs) and automatic distribution according to the goals of reliability, operation, and economy of the distribution system. The objective function aims at minimizing the total costs of construction, maintenance, and operation of distribution automation resources and devices, plus the cost of voltage deviations and expected energy not supplied in this paper. This scheme limits to AC optimal power flow, the planning constraints of DGs and distribution automation devices, and reliability equations. The mentioned scheme has an integer nonlinear optimization format. In the following, a linear approximation model is extracted for it to reach the unique response. Finally, by applying the proposed problem to the standard distribution grid by GAMS optimization software, the numerical results highlight the capability of the proposed scheme in improving the technical and economic conditions of the distribution network with optimal DGs and distribution automation planning. [ABSTRACT FROM AUTHOR]

Microgrids are a viable substitute for traditional power systems because they may deliver cleaner, more dependable, affordable power with fewer losses. However, the microgrid's performance is impacted by the variable nature of renewable energy sources. Battery storage is a crucial component of microgrid planning since it defines the system's techno‐economic feasibility. A standalone rural microgrid is designed in the current study, employing three distinct battery types: lithium‐ion, lead acid, and zinc‐bromine flow. The suggested microgrid's techno‐economic analysis employs three distinct dispatch mechanisms, that is, cycle charging, load flow, and complete dispatch. The case study of the suggested framework is carried out in Lucknow (India). The system comprises PV/battery/wind energy system/diesel and battery. The simulation results suggest that the optimal system with the least electrification cost is 0.113 $/kWh using complete dispatch strategies and a zinc‐bromine battery. It has 206 kWh zinc‐bromine flow batteries, a 10 kW converter, a 20 kW PV, a 13‐kW diesel generator, and a Combined dispatch strategy. The system's net present cost per unit cost of energy is $40 275 and 0.113 $/kWh for the chosen region, respectively. Compared to the other two battery technologies for hybrid systems, the zinc‐bromine flow battery technology is also shown to be the most environmentally friendly. Among the three battery technologies available, zinc‐bromine flow is best used in a particular location's hybridized operation. [ABSTRACT FROM AUTHOR]

Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the "closer-to-reality" (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods. [ABSTRACT FROM AUTHOR]

Overloaded trucks contribute heavily to road damage and increased maintenance costs, and Weigh-In-Motion (WIM) systems are an effective tool for detecting them without disrupting traffic flow. However, overloaded truck drivers often adjust their routes to avoid WIM stations, complicating enforcement efforts for road management stakeholders. To address these challenges, this study integrates the strategic behaviors of multiple stakeholders with diverse objectives into optimal WIM planning by modeling interactions among the government, pavement management agencies, and drivers. The authorities are responsible for WIM installation, while drivers minimize their respective travel costs. The proposed approach considers both road maintenance costs incurred by authorities and travel costs for drivers, based on a traffic assignment model for each WIM installation strategy. Basic concepts from game theory are adopted to formalize the dynamic interactions among these stakeholders. [ABSTRACT FROM AUTHOR]

Since renewable energy is rapidly growing in the active distribution networks, the integrated energy system coupled with energy storage is a promising way to address the intermittent issues of renewable sources. This paper proposes an optimal planning model for the hydrogen‐based integrated energy system (HIES) considering power to heat and hydrogen (P2HH) and seasonal hydrogen storage (SHS) to take full advantage of multienergy complementarity. To tackle the unstable factors introduced by renewable sources and varying loads, we apply robust optimization and stochastic programming theory to improve the robustness of the planning results. Meanwhile, we also consider the N‐1 contingency constraints to make the technology selection, capacity allocation, and economic operation more reliable. The complex constraints resulting from producing two independent binary variables are converted into mixed integer linear constraints, which can be solved effectively using the nested column‐and‐constraint generation algorithm. Numerical simulation demonstrates the effectiveness of the P2HH and SHS in reducing the total cost of the HIES planning. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of the history of the Soviet economics and mathematics school, its struggle with official ideology and attempts to influence the choice of strategies for socioeconomic development. During the NEP period, a galaxy of brilliant economists worked in the Soviet Union, who mastered advanced statistical and economic–mathematical methods and obtained a number of fundamental results, which, under favorable circumstances, could have become the basis for Russia's inclusion in the global stream of economic research. However, the leaders of the emerging trend advocated a rational combination of plan and market, which contradicted the government's decision to curtail the NEP. The authorities demanded justification for their policies, independent research was viewed as hostile, and the school was destroyed. Its revival began in the late 1950s after the exposure of the cult of personality and proceeded in a fierce struggle against pedantic Marxism. It is shown that this struggle revealed the imperfection of the world economic and mathematical theory of that time, which was focused on the study of market competition and did not consider the mechanisms of rationing, queues, and the black market, typical for a planned economy. [ABSTRACT FROM AUTHOR]

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity. [ABSTRACT FROM AUTHOR]

The scale and cluster development of offshore wind power faces the constraints of high construction cost, limited submarine corridor resources, etc. Under the background of offshore wind power parity development, it is urgent to consider the actual constraints of the project and the impact of project income on the owner, and the construction of offshore public station and the optimal selection of transmission method have become an important measure to reduce the cost and increase the efficiency of offshore wind power. A life-cycle cost model for offshore wind farm cluster grid-connection system planning is constructed to consider the project income, and the offshore public station location and capacity, transmission method, and high-voltage cable selection and path are optimized, which are solved by an improved single parent genetic algorithm and a topology repair strategy based on graph theory. The results show that the proposed planning scheme can effectively improve the life-cycle project income of the grid-connection system and provide technical support for the grid-connection planning of large-scale offshore wind farm clusters. [ABSTRACT FROM AUTHOR]

Trajectory optimization is a complex process that includes an infinite number of possibilities and combinations. This work focuses on a particular aspect of the trajectory optimization, related to the optimization of a velocity along a predefined path, with the aim of minimizing power consumption. To tackle the problem, a functional formulation and minimization strategy is developed, by means of the Euler-Lagrange equation. The minimization is later performed using a neural network approach. The strategy is deemed Lagrange-Net, as it is based on the minimization of the energy functional, by the means of Lagrange's equation and neural network approximations. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 study reviews the steps Canada is taking to address sustainable decarbonization in the context of carbon capture. This work also presents a new optimal framework for national optimal deployment in need of strategic carbon capture implementation. This framework considers external environmental and social considerations often missing from implementation frameworks, which will aid policy makers in more well‐rounded deployment decisions. Thus far, Canada's carbon projects have captured a total of 36.3 Mt of CO2 which has cost over $2.7 billion to implement. The Canadian case study utilizing the proposed optimal planning strategy shows that implementation of 58 post‐combustion carbon capture (PCC) plants located in seven provinces (Alberta, British Columbia, New Brunswick, Nova Scotia, Ontario, Quebec, and Saskatchewan) would result in Canada meeting the national targets. This implementation includes 16 plants removing emissions from the Electricity sector, 16 from the Heavy Industry sector, and 26 from the Oil and gas sector resulting in new emissions levels of 11.82 MtCO2, 27.63 MtCO2, and 107.01 MtCO2 in each sector, respectively. Additional case studies examined the impact of Alberta's emissions and varying the national targets resulting in different optimal implementations plans. Through a sensitivity analysis on these targets, it was determined that plant distribution is heavily dependent on provincial energy and CO2 transport prices. Additionally, if Alberta were to reduce their GHG emissions by 50% through alternative sustainable methods, only 35 PCC plants would be required to meet national targets. This framework provides a sustainable tool for decision‐makers to accelerate decarbonization. [ABSTRACT FROM AUTHOR]

Due to the climate change, there have been changes in the rhythm of nature over the last decades. In addition, there has been an increasing social consciousness regarding decarbonization and emissions, promoting governments new policies and economic support measures for renewable integration. In this scenario, the participation of the onshore and offshore wind sectors play a crucial role, expecting that more than 6000 GW will be globally installed in 2050. In parallel, the useful life of other wind power plants is coming to their end. Therefore, some countries will face in the coming years the decision of repowering, decommissioning, installing new turbines, or a combination of them. In general, the areas with the greatest wind potential are already occupied by relatively old wind turbines. Subsequently, a complex group of factors influences on the most appropriate solution decision-making, involving climatic, technological, environmental, social or economic aspects. This paper proposes an optimized methodology based on multi-criteria decision-making to estimate the most appropriate solution for extending the useful-life of wind power plants. An initial group of 26 factors were identified, corresponding to seven categories: technical, geographic, socio-environmental, location, economic, political and climate. The methodology is divided into three stages: (i) data, (ii) alternatives, and (iii) optimization. It was assessed with a Spanish onshore wind power plant connected to the grid. From the initial data and factors, 11 alternatives were designed based on a selection of 10 most relevant factors. The results provide an optimal solution, combining both repowering and installation of new wind turbines. From the results, this methodology would allow reducing the LCOE of the new wind plants installed in the European Union by more than 50%.

The volatility of renewable energy generation accelerates the aging of water distributionally robust optimization within Power-to-Gas （P2G） stations. Existing optimization models fail to effectively account for the impact of wide power fluctuations on the service life of water electrolysis equipment and the capacity configuration of P2G stations. In this context， this paper proposes a distributionally robust optimization model for the capacity configuration in P2G stations， taking into consideration the service life of electrolysis equipment. Firstly， a model for assessing the degradation of water electrolysis equipment service life is introduced， considering the influence of power fluctuations. Subsequently， an optimization and planning model for an integrated electricity and natural gas system （IENGS） that includes P2G stations is established. This model optimizes the capacity configuration of electrolysis， methanation， and hydrogen storage tanks within the P2G station. Secondly， a fuzzy set of probability distribution for source-load scenarios is constructed based on l1-norm constraints and l∞-norm constraints. The planning model is reformulated as a min-max-min distributionally robust optimization （DRO） model and solve it using a column-and-constraint generation algorithm. Finally， the proposed model and algorithm are validated using test cases. Results demonstrate that the proposed model can more accurately calculate the economic benefits of P2G stations and extend the expected service life of electrolysis equipment.

Abstract Trajectory planning aims at computing an optimal trajectory through the minimization of a cost function. This paper considers four different scenarios: (i) the first concerns a given trajectory on which a cost function is minimized by a acting on the velocity along it; (ii) the second considers trajectories expressed parametrically, from which an optimal path and the velocity along it are computed; (iii), the case in which only the departure and arrival points of the trajectory are known, and the optimal path must be determined; and finally, (iv) the case involving uncertainty in the environment in which the trajectory operates. When the considered cost functions are expressed analytically, the application of Euler–Lagrange equations constitutes an appealing option. However, in many applications, complex cost functions are learned by using black-box machine learning techniques, for instance deep neural networks. In such cases, a neural approach of the trajectory planning becomes an appealing alternative. Different numerical experiments will serve to illustrate the potential of the proposed methodologies on some selected use cases.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy can promote the accommodation of green electricity. This paper proposes an optimal capacity planning approach for an industrial electricity-hydrogen multi-energy system (EHMES) aimed to achieve the local utilization of RES and facilitate the transition to carbon reduction in industrial settings. The proposed approach models the EHMES equipment in detail and divides the system's investment and operation into producer and consumer sides with energy trading for effective integration. Through this effort, the specialized management for different operators and seamless incorporation of RES into industrial users can be achieved. In addition, the variations in investment and operating costs of equipment across different installed capacities are considered to ensure a practical alignment with real-world scenarios. By conducting a detailed case study, the influence of various factors on the capacity configuration outcomes within an EHMES is analyzed. The results demonstrate that the proposed method can effectively address the capacity configuration of equipment within EHMES based on the local accommodation of RES and variable unit cost sequence. Wind power serves as the primary source of green electricity in the system. Energy storage acts as crucial equipment for enhancing the utilization rate of RES. [ABSTRACT FROM AUTHOR]

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low. [ABSTRACT FROM AUTHOR]

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity.

The scale and cluster development of offshore wind power faces the constraints of high construction cost, limited submarine corridor resources, etc. Under the background of offshore wind power parity development, it is urgent to consider the actual constraints of the project and the impact of project income on the owner, and the construction of offshore public station and the optimal selection of transmission method have become an important measure to reduce the cost and increase the efficiency of offshore wind power. A life-cycle cost model for offshore wind farm cluster grid-connection system planning is constructed to consider the project income, and the offshore public station location and capacity, transmission method, and high-voltage cable selection and path are optimized, which are solved by an improved single parent genetic algorithm and a topology repair strategy based on graph theory. The results show that the proposed planning scheme can effectively improve the life-cycle project income of the grid-connection system and provide technical support for the grid-connection planning of large-scale offshore wind farm clusters.

The increasing integration of distributed household photovoltaics (PVs) and electric vehicles (EVs) may further aggravate voltage violations and unbalance of low-voltage distribution networks (LVDNs). DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple power quality problems by the flexible power regulation capability of voltage source converters. This paper proposes schemes to establish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC operation. The characteristics and DC conversion constraints of typical LVDNs are analyzed. In addition, converter configurations for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations. Moreover, an optimal planning method of hybrid AC/DC LVDNs is proposed, which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance. Simulations are conducted to verify the effectiveness of the proposed optimal planning method. Simulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reducing issues related to voltage violation and unbalance.

With the increasing adoption of electric vehicles, it becomes crucial to ensure that Fast Charging Stations (FCSs) are strategically located and adequately sized to establish a dependable and accessible charging infrastructure. This study investigates the critical issue of optimizing the size and location of these stations, considering total costs, including investment costs, operation and maintenance costs, cost of power system loss, and power system reliability costs. To address this challenge, Particle Swarm Optimization (PSO), a powerful optimization technique, is employed to identify the optimal sizes and locations of FCSs. The PSO enables the consideration of complex relationships among variables such as Electric Vehicles (EVs) demand, station capacity, and power system limitations. From the perspective of the Transmission System Operator (TSO), the impact of FCSs on power system reliability has been evaluated using an Energy Not Supplied (ENS) index to evaluate power system reliability costs. Simulation results on the 193-bus test system in northeastern Thailand, conducted with Power Factory DIgSILENT software, validate the effectiveness of our proposed model and method. The results demonstrate that our approach not only identifies the optimal placement and size of FCSs with minimal overall costs but also ensures the operational security of the power system. The proposed method can solve the problem rapidly and accurately. Additionally, power system reliability is considered a criterion in the proposed method.

Overloaded trucks contribute heavily to road damage and increased maintenance costs, and Weigh-In-Motion (WIM) systems are an effective tool for detecting them without disrupting traffic flow. However, overloaded truck drivers often adjust their routes to avoid WIM stations, complicating enforcement efforts for road management stakeholders. To address these challenges, this study integrates the strategic behaviors of multiple stakeholders with diverse objectives into optimal WIM planning by modeling interactions among the government, pavement management agencies, and drivers. The authorities are responsible for WIM installation, while drivers minimize their respective travel costs. The proposed approach considers both road maintenance costs incurred by authorities and travel costs for drivers, based on a traffic assignment model for each WIM installation strategy. Basic concepts from game theory are adopted to formalize the dynamic interactions among these stakeholders.

Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the “closer-to-reality” (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods.

Utilizing wind power (WP) for hydrogen production can alleviate wind curtailment and improve wind energy utilization. The optimal planning of hydrogen-storage units(HSUs) in wind–hydrogen energy system(W-HES) will affect its economic operation. This paper proposes a W-HES model comprising WP, HSUs, and loads. We then utilize clustering algorithm-based WP and power load scenario division method. And taking the minimum investment, operation, and maintenance cost of W-HES as the objective function, use the particle swarm optimization(PSO) algorithm to optimize the planning of HSUs of W-HES. Finally, we investigate the impact of diverse hydrogen energy sources and daily hydrogen load variations on the optimal planning outcomes of HSUs. It will provide a valuable insights for the planning and configuration of HSUs in W-HES.

With the increase in world population and limited energy resources, countries have faced the high demand of energy and energy consumption problem. The crisis that threaten countries and human societies are the limited resources of non-renewable (fossil) energy and the increase in environmental pollution caused by excessive consumption of fossil fuels and global warming. These factors have motivated researchers and investors in the energy sector to control and supply energy from renewable sources. The uncertainty caused by these generations can have many effects on the costs imposed on the network and the operation of the electricity networks, such as an increase in power outages and unsupplied energy. Network development planning is one of the important issues in the power system to meet the growth of electricity demand in the coming years due to urban development, increasing social welfare, energy security, and job creation. The final objective of this model is to minimize energy losses, investment and operating costs, unsupplied energy, and environmental pollutants. The proposed methods have been implemented by MATLAB software on the Garver electricity network and the IEEE 33-bus distribution network and solved by PSO algorithms. The final model can be effectively used for planning the supply chain of the conventional electricity network with the penetration of renewable energy-based generations in various economic, environmental, and social dimensions.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

The current scenario sees the potential emergence of challenges such as power imbalances and energy dissipation upon the incorporation of distributed photovoltaic (PV) systems into distribution networks, impacting power quality and economic viability. To address these identified risks, this study introduces an innovative combinatorial search algorithm designed to autonomously derive optimal planning strategies for distribution networks. The process begins by establishing distinct planning models for distributed PVs and distribution network systems, followed by the application of the search algorithm to align these models and generate relevant Pareto datasets and multi-objective positioning criteria. By strategically combining and optimizing existing solutions to bolster the distribution network's load-carrying capacity, the optimal strategy is progressively refined under the guidance of multiple objective constraints. Subsequent multiphase simulation experiments validate the efficacy of our approach in minimizing energy losses when compared to analogous methodologies. The distribution network integration planning strategies derived through our method showcase outstanding performance in reducing energy losses, conducting steady-state voltage safety assessments, optimizing installation capacity utilization rates, and enhancing economic returns, thus emphasizing the substantial potential of our approach in facilitating the seamless integration of distributed PVs into distribution networks. [ABSTRACT FROM AUTHOR]

China will strive to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060, which requires the development of a power network with high penetration of renewable energy generation. With the addition of new types of load technologies and larger peak-valley difference due to the development of economy, the uncertainty and unbalance between generation and load becomes a salient problem for low-carbon power networks. Energy storage devices can provide flexibility to such power networks and are considered to be a vital part in the construction of low-carbon power systems. In this research, a planning method for large-scale electro-chemical energy storage devices in networks considering battery ageing is developed, and the assessment of energy storage based on simulation is performed. This method can provide guidance for the configuration of energy storage devices in future low-carbon power networks. The addition of energy storage can lower the cost of system operation and decrease the emission of greenhouse gases. The reliability of the system and the ability of renewable energy consumption can be enhanced.

Abstract Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

To meet increased demand in energy while reducing greenhouse gas emissions different renewable energy technologies are used. The wind power provides potential benefits as it is a clean, renewable, economic and domestically available power source. Building of sustainable wind farm is subject on many different factors. The aim of the paper is to contribute optimal turbines placement and integration to the electrical grid. For the goal, a mixed-integer non-linear optimization model for determination of maximum wind farm capacity is defined. Another linear optimization model is used to determine the minimum distance of wind turbines in the farm to the point of common coupling. The proposed approach is tested numerically for particular wind turbines type and given wind farm area. The testing results demonstrate the applicability of the proposed optimization models for determination of maximum wind farm capacity and minimum distance between wind farm and point of common coupling.

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system's operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model's scale. To ensure an efficient solution, a Benders' decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM's siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits. [ABSTRACT FROM AUTHOR]

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Vision transmission systems (VTS) manages to achieve the optimal information propagation effect given reasonable strategies. How to automatically generate the optimal planning strategies for VTS under specific conditions is always facing challenges. Currently, related research studies have dealt with this problem with assistance of single-modal vision features. However, there are also some other information from different modalities that can make contributions to this issue. Thus, in the paper, we propose a data-driven optimal planning scheme for multimodal VTS. For one thing, the vision features are employed as the basic mechanism foundation for mathematical modeling. For another, the data from other modalities, such as numerical and semantic information, are also introduced to improve robustness for the modeling process. On such basis, optimal planning strategies can be generated, so that proper communication effect can be obtained. Finally, some simulation experiments are conducted on real-world VTS scenes in simulative platforms, and the observed simulation results can well prove efficiency and proactivity of the proposal.

With the intent of reducing carbon emissions, renewable-rich microgrids, in which most of the load is being served by renewable sources, are expanding rapidly to achieve maximum self-sustainability and minimum dependency on the grid. However, the area required to commission such a renewable-rich microgrid is a major constraint, particularly for the long-term planning of microgrids in urban areas, in addition to the criticality of cost and availability. In this connection, this paper presents a framework for optimally planning a renewable-rich hybrid microgrid with area constraints for energy resources. Considering the criticality, the proposed framework includes the formulation for optimization of twin objectives: the cost of the microgrid and the availability of power in the microgrid. The problem is solved using a Multi-objective approach to minimize the cost, maximize availability, and importantly achieve a feasible generation mix subjected to specified area constraints, a first of its kind. Stochastic algorithms based on genetic and swarm techniques are employed to solve the multi-objective optimization problem of planning a renewable-rich hybrid microgrid. From the performance analysis, a superior method is identified which is further applied for expansion planning of the considered hybrid microgrid to meet future requirements with limitations on area constraints while taking advantage of modularity associated with renewable sources, a first of its kind. To handle the uncertainties associated with such renewable-rich microgrids, uncertainty analyses are carried out by constructing the utility function and validated using Monte Carlo simulations to rationalize decision-making on system design. It is to note that the results from the proposed method of microgrid planning are found to be effective over the known normal distribution of the considered uncertainty as well. The modeling, decision-making, and uncertainty analyses, are carried out with the help of MATLAB environment.

Fast charging stations (FCSs) for electric vehicles are associated with large power demands, often coinciding with peak power demands from other consumers. This places significant strain on distribution networks (DNs). To address this issue, this paper proposes a promising method that harnesses the untapped potential of electric railway systems (ERSs) and wasted regenerative braking energy (RBE) of trains in combination with a battery energy storage system (BESS) to supply EV charging station and participate in the FCS energy supply chain (FCS-ESCH). The proposed integrated approach, as the intermodal sustainable electric transportation system (SETS), aims to reduce the peak power demand of FCSs especially at strategic locations, such as parking areas close to electric railway stations and park-and-ride areas. To achieve this goal, an operation-aware optimization model is developed that determines the optimal sizes of the interfacing converter and the BESS, while also proceeding with optimal energy exchange planning. A generic model for trains’ RBE production is proposed, as well, and adapted to the operation-aware optimization model to verify the effectiveness of the proposed approach by conducting simulations of a real case study that accurately replicates system behavior with the desired level of precision. Furthermore, sensitivity analyses are performed to explore the impact of varying the power rating of the interfacing converter responsible for transmitting RBE to the FCS-ESCH, as well as the per-unit price of BESS energy capacity. The results showcase the potential of employing SETS as an efficient means to mitigate the challenges associated with FCSs, especially peak shaving and cost reductions, paving the way for a more sustainable electric transportation system.

The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city's infrastructure. An experimental evaluation was conducted considering Naples's fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19 ∗ 10 12 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps. [ABSTRACT FROM AUTHOR]