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Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.

This work aims to improve the voltage profile and reduce electrical network losses through optimal planning of distributed generators. A new search algorithm (Autoadd) along with the (PSO) are introduced to choose the best location and size for distributed generators. Two systems are implemented; a 33-bus test network and a 30-bus of a local community in the city of Al- Diwaniyah. At the power flow, a solution is implemented using a fixed-point iteration method within an OpenDSS environment to check the performance of both networks. Moreover, the optimal location and size of the distributed generators are determined using Autoadd and PSO methods. The Autoadd method is implemented within the OpenDSS environment, while the (PSO) method is implemented within the MATLAB-OpenDSS environment through the com-interface. The validity and effectiveness of the proposed methods are validated by comparison with the published researches. The results have proven that the fixed-point method has achieved high efficiency and accuracy in terms of analyzing the power flow, whereas the (Autoadd) algorithm has achieved a better effect in terms of improving the voltage profile and minimizing losses.

Determining the optimal location and capacity of energy storage systems (ESS) is a crucial planning problem for the virtual power plant (VPP). However, the trading characteristics of VPP have not been considered in the existing ESS optimal planning research due to its complicated mathematical relationships. In this paper, considering the VPP trading environment, a bi-level optimization model is established to determine ESS’s optimal location and capacity. The proposed model is a mixed-integer, nonlinear, multi-period, and large-scale optimal problem, which is difficult to solve directly by traditional methods. Usually, linearizing or decomposing methods are used to simplify the original problem for solving. However, the available simplification methods greatly change the original problem, resulting in a large deviation between the simplified and the original problems. This paper proposes a novel interactive solution framework for the bi-level model based on the decomposition-coordination algorithm for improving the solution accuracy. The simulation results show the effectiveness of the ESS planning scheme in VPP under different objectives, EVs types, and electricity prices. Further analyses based on the IEEE 17-bus VPP system demonstrate that the proposed method can save nearly 29.01%, 28.08%, 26.62% of the ESS cost on average compared with the loss sensitivity factor, MILP-Based, and Benders decomposition methods, respectively.

Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

Our aim in this study is to propose a novel approach for optimizing a variant of the home health care routing problem, in which the health-care workers perform a requested number of visits to patients by using electric vehicles. Our approach on the one hand uses multi-objective optimization methods to find optimal planning routes of EVs and on the other hand deals with uncertainty in some major problem constraints. The goal is to help decision makers choosing appropriate routes; while minimising the cost of electric vehicular displacements in terms of: the total travel cost for all home-health workers and the waiting time for all patients. Moreover, we consider in our problem the fuzzy nature of some major constraints.

For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of power-distribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users’ convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CS-allocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users’ satisfaction and for better station utilization.

Energy internet zones integrate various distribution generators and energy kinds together as whole to satisfy consumers’ different energy requirements. It can achieve higher energy efficiency, but suffers various uncertainties, like renewable power and energy demands. Because the time scope is several years, the uncertainties are difficult to precisely obtained. Thus, this paper adopted a interval optimal planning strategy for energy internet zones. The uncertainties are modeled by interval numbers. The planning strategy determines the optimal configuration and optimize the energy internet zone’s cost intervals. The interval numbers’ ordering is defined by decision maker’s degree of pessimism for risk aversion. A case study is Shanghai is adopted to illustrate the proposed model can minimize the energy internet zone’s possible cost intervals and manage risk.

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity-transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi-sequential Monte Carlo simulation is presented. This study proposes a novel reliability-oriented multi-objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large-scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method.

In the recent decade, a significant increase in the penetration level of renewable energy sources (RESs) into the distribution grid is evident due to the world's shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system. RES based microgrids are the most favorable option available, especially to enhance resiliency. However, the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions. During extreme weather conditions, RES behavior is completely uncertain. Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions. To address these issues, many researchers proposed several methods to place energy storage units (ESUs) and microgrids (RES integrated), which can support critical loads at an optimal location in the distribution system during normal and extreme conditions, respectively. The aim of this article is to consolidate and review the research towards various approaches to formulate the problem (optimal location, allocation, and operation of ESU and microgrids to face regular and extreme weather condition) and tools to solve it for enhanced system flexibility and resiliency. Based on the review, a generalized methodology has been designed to adapt the inputs and address both conditions. At the end of the review, future aspects for ESU to strengthen resistance and resiliency of its own are presented, which can be helpful to further improve the reliability and resiliency of the distribution system.

For highly reliable products and devices, conducting the life tests at normal usage conditions demands a great deal of time and cost, which can result in missed opportunities to introduce the produ...

A key concern in the design of 5G is the radio access network, which is expected to be significantly denser and more advanced, with considerably higher infrastructure and power consumption cost than that of conventional mobile network standards. Novel algorithms/approaches for optimal planning of the radio access network are required for tackling the additional complexity of the problem of cost‐efficient radio access planning in 5G, which cannot be properly handled by conventional approaches. This study proposes a novel optimisation framework for the cost‐efficient deployment and configuration of 5G base stations. The main idea of the proposed optimisation framework is to exploit heterogeneity in three key 5G technologies, heterogeneous base station architecture, cell range extension and multiple‐input–multiple‐output spatial multiplexing, by jointly optimising their configurations during network design. In addition, the proposed optimisation framework includes generic steps for applying meta‐heuristic algorithms to the problem, which are necessary to overcome the problem's complexity, especially for large problem instances. The authors’ results show that their novel optimisation framework improves the cost efficiency of the network planning both in terms of power and infrastructural cost to operators.

Planning and operation of smart energetic projects have increased in complexity and requires a deeper analysis. The inclusion of renewable energy and distributed generation sources lead to an inher...

Time-management refers to planning the available time in correspondence with personal goals and preferences of the employee. The criterion for efficient time-management lies in achieving definite goals in minimum possible time. This paper presents a short review of the literature on time-management. It is shown how scheduling theory may be used for optimal planning in time-management. The problem of minimizing the total (average) weighted completion time of the planned jobs by an employee is considered provided that only lower and upper bounds of the possible processing time of each job are known before scheduling. Software for constructing a permutation of the given jobs with the largest relative semi-perimeter of the optimality parallelepiped has been developed. Computational experiment on the computer showed the effectiveness of the developed algorithms for constructing schedules for time-management.

We investigate the complex optimization problem that arises in the planning of the transition process from traditional public transport to electric transport. We define the assumptions, input and output parameters of the problem, as well as its mathematical model and a randomized algorithm for solving it. We also give an extensive bibliography of publications on the problem at hand.

Purpose. The purpose of the article is to develop a universal unified parallel synchronous algorithm for the implementation of tasks for calculation of maximum one- and multicommodity flows, as well as the creation of a software complex that provides the formation of surface graph models of flows and performs optimal planning of non-uniform flows in transport and other networks. Methodology. The paper investigates the possibilities of previously created and comprehensively verified heuristic parallel synchronous algorithm for calculating maximum one- and multicommodity flows in the networks, establishes its potential limitations, and determines additional advanced procedures that transform the algorithm into a universal parallel algorithm. The proposed parallel synchronous algorithm uses a width-first search strategy while simultaneously identifying possible paths of flows through the network with an estimation of their throughput. Herewith the possibility of analyzing several incremental flows across the network in one iteration was studied. Findings. The article proposes a universal unified parallel synchronous algorithm for calculating maximum flows in networks and develops a unified procedure and software package for planning of non-uniform as well as competitive flows in transport and other networks. The developed software complex implements the problems of formation of surface graph models of networks, for which the problem of optimal planning of non-uniform and competitive multicriteria flows in transport networks is solved. Originality. The article develops a new universal unified parallel synchronous algorithm and procedure for the calculation of optimal uniform, multicommodity and competitive flows in transport networks. Practical value . The practical value of the obtained results is determined by the universal capabilities and efficiency of the procedure for planning non-uniform flows in the networks based on the application of a new parallel synchronous algorithm, as well as the developed software complex, which provides the ability to solve the problems of analysis and planning of uniform and multicommodity flows in transport networks, as well as the implementation of calculation tasks of competitive models of transport and information flows formation. The software complex has a built-in editor of interactive network modeling and a toolbar, which provides both creation of new and downloading existing graphs of networks from the modeling libraries, preservation of optimum flows in the network in the form of an image and a text file, output of errors when working with the program.

The generation expansion planning (GEP) is a very relevant optimization problem in power systems research. Several factors must be considered in the management of an optimal planning that aims at expanding the generation, such as satisfying a growing demand, dealing with costs of investment, operation and maintenance, dealing with pollutant emission, among others. Considering these aspects, this problem tends to be very complex and meta-heuristics methods have been employed in order to provide satisfactory solutions. This paper aims at reviewing GEP considering five different formulations of the problem and how meta-heuristics approaches can be applied to it. For this purpose, two case studies are presented and, in order to provide a deep analysis, these methods are compared according to their performance, the solution provided by them, and also comparing results obtained among different formulations, which leads to a promising methodology and some interesting results provided.

Optimal planning of Inspection, Maintenance and Rehabilitation (IM&R) of civil engineering structures has been the focus of specialised literature during the last decades. More recently, permanent ...

With the development and application of large-scale distributed generation and new electrify transportation loads such as electric vehicles and electric ships, the intermission and randomness of distributed generation and transportation loads lead to enormous challenges in the operation and planning of distribution network. In this paper, a multi-objective planning model is proposed for the distributed generations that considers the transportation loads and the typical source-load spatiotemporal scenarios. Firstly, the probability model of distributed generation and various electric loads are established. And the typical source-load spatiotemporal scenarios are established according to the probability theory and the multi-scenario analysis method. Then three objective functions are represented for the planning model, the objective functions includes the economic objective minimal average annual comprehensive cost of distributed generation, the technical objective maximal comprehensive utilization rate and the security objective minimal average voltage offset of the bus. The multi-objective function is solved by a hybrid multi-objective algorithm, which combines Particle Swarm Optimization, Simulated Annealing. Finally, A IEEE-33 bus system in port area of China is employed to verify the effectiveness of the prosed model and algorithm.

The continuous growth in the number of electric vehicles (EVs) places higher demands on public charging station (PCS) planning. This study proposes a PCS locating and sizing method based on the discrete distribution of EV charging demand. First, the distribution of EV charging demand is predicted by the distribution of gas station sales, functional areas, and population density. Next, a method of determining the PCS capacity based on the distribution of the charging demand is given. Then, the PCS locating and sizing model with the goal of minimising the total social cost is established, and the particle swarm optimisation algorithm is used to solve the problem. Finally, the method is verified by a case in Beijing. The results show that after obtaining several convenient parameters and using a simple algorithm calculation, this method can effectively reduce the construction and operation costs of PCSs and give full play to the social benefits of PCSs.

As an islanded multi-microgrid (IMMG) receives little support from the main network, it is necessary to develop new dispatch frameworks to fulfil its real-time dispatch requirement. Hence, a decentralised collaborative dispatch framework of IMMG based on multi-agent consensus algorithm is proposed. In the framework, the ttal power command of IMMG can be optimally allocated among all the microgrids (MGs) based on consensus algorithm. Then the power command of each MG can be efficiently allocated among all the dispatchable units based on the proposed priority-based collaborative dispatch strategy. Based on the decentralised collaborative dispatch framework, in order to optimise the planning and operation of dispatchable active power resources (DAPRs), on one hand, a bi-level model that couples the planning and operation problems of an IMMG is established to optimise the configuration of DAPR; on the other hand, more detailed models are proposed for the regulation costs of various dispatchable units, which is an improvement over the existing regulation cost model. By considering the two aspects, the consensus algorithm is embedded in multiple population genetic algorithm to solve the bi-level model. Case studies are conducted on an IMMG consisting of three MGs to examine the effectiveness of the proposed planning and dispatch model.

Water being one of the most important elements for life on earth and the planetary ecosystem as a whole, it is widely recognized by both society and the United Nations as one of the most important research problems of the modern world. One of the difficulties faced by this system has to do with the management of water treatment plants in the context of sustainable development. This article introduces a decision-making support system conceived to develop an optimal planning aimed at the efficiency and sustainability of the drinking water treatment chain. The system is made up of a mathematical programming model and a solution procedure for the optimal planning of the water treatment in a bi-objective context. Thus, the first objective maximizes profit and the second one minimizes the emission of pollutants. The following constraints are considered: mass balances, production capacities of the different water production echelons and their specific conditions, water supply and demand, and the technically permissible levels of pollutants, which are generically suggested, regardless of the technologies and production alternatives. Due to its particular nature, the model allows solving any instance of the problem in excellent CPU times.

Battery Energy Storage Systems can provide various applications in the distribution networks including load leveling, expansion deferral, voltage profile improvement, frequency regulation, fast reserve, and renewable energy time shift. Despite offering various applications, the BESSs are characterized with high investment costs. An optimal investment plan can justify the cots by exploiting multiple simultaneous applications. To this end, this paper proposes a novel planning model for the BESSs in distribution network wherein both load leveling and voltage profile improvement applications are extracted. The load leveling application refers to storing cheap energy at off-peak periods to deliver at peak period with high energy costs. In this way, the load profile tends to be flat and resulting lower energy cost. The voltage profile improvement is achieved by the optimal reactive power control of the inverter located between the BESS and the grid. The idea is that the inverter can allocate rated power capacity to the reactive power exchange at periods with negligible or zero active power conversion. Simulation results demonstrate that the voltage profile can improve in some degrees without increasing planning and/or operation costs. Higher degrees of improvement can be achieved at the expanse of cost increase. Also, the simulations show that the conducted load leveling results to loss reduction, peak shaving, and network expansion deferral subsequently. - 2019 Elsevier Ltd This publication was made possible by NPRP Grant no. 11S-1125-170027 from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. Scopus

This manuscript aims to present a comprehensive literature reviews of various aspects for hybrid microgrids (HMGs) comprising mathe modeling, different optimization techniques, and common adapted objective functions along with their equality and inequality constraints and so on. Classical and modern optimization methodologies are recognized with their inherent features. Special care of renewable energy sources expressly wind and solar including energy storage systems are in order. In addition, uncertainties in solar and wind energy resources are highlighted, and the applications of forecasting models are presented. Comparisons among various HMGs planning and design methods are summarized and criticized for the sake of concluding their merits and demerits. Finally, technical advices for giving good insights for HMGs designers and future researches in this regard are emphasized.

This work considers optimal planning of progressive type-I interval censoring schemes for log-location-scale family of distributions. Optimum schemes are obtained by using a Bayesian C-optimality design criterion. The C-optimality criterion is formed to attain precision in estimating a particular lifetime quantile. An algorithm is proposed to obtain the optimal censoring schemes. Optimal schemes are obtained under two different scenarios for the Weibull and log-normal models, which are two popular special cases of log-location-scale family of distributions. A sensitivity analysis is conducted to study the effect of various prior inputs on the optimal censoring schemes. Furthermore, a simulation study is undertaken to illustrate the sampling variations resulting from the optimal censoring schemes.

Residual biomass is a renewable resource with attractive characteristics to produce energy and biofuels. Diverse studies have stated that residual biomass used for biofuels and energy production ca...

Waste management can be considered as a strategic supply chain problem as it involves the waste generation, collection, separation, transportation, treatment, distribution, and disposal. This paper presents a mixed integer linear programming model for the coordination of tactical and operational decisions in waste supply chain networks including the logistics, production, and distribution. The model aims at maximizing the profit of the entire supply chain while satisfying the demand, production, transportation, and inventory constraints imposed by different entities of the network. A case study is selected to test the effectiveness and efficiency of the proposed model. Such an integrated network contributes to the maximum utilization of recyclable waste including paper, plastic, glass, and metal processed by recycling plants, and non-recyclable waste treated in waste to energy plants. Sensitivity analyses are performed to investigate how changing parameters including the time periods and products’ prices affect the supply chain performance.

In 2019, there were 759 million people globally without access to electricity and 2.6 billion people lacked access to clean cooking. Cooking with electricity could contribute to achieving universal access to energy by 2030. This paper uses geospatially-based techniques—a computer model named REM, for Reference Electrification Model—to show the impact of integrating electric cooking into electrification planning. Three household scenarios were analyzed: one for providing basic electricity access with no electric cooking; another for cooking with electricity; and the third for cooking half of the meals with electricity and half with another fuel, with a clean stacking process. Results of the application of REM to the three scenarios were obtained for the Nyagatare District, Rwanda. The case study showed that electric cooking substantially changes the mix of technologies and the total cost of the least-cost electrification plan. It also showed that electric cooking can be cost competitive compared to LPG and charcoal in grid-connected households and can reduce greenhouse emissions. Stacking with energy-efficient electric appliances provides most of the benefits of full electric cooking at a lower cost and is a pathway worthy of further consideration.

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

Some international pharmaceutical companies have succeeded in producing vaccines against COVID‐19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K‐independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model.

Purpose This paper aims to propose, the multi-objective method for optimal planning and operation of distributed generators (DGs) on distribution system (DS) using hybrid technique is proposed. Design/methodology/approach The proposed hybrid technique denotes hybrid wrapper of black widow optimization algorithm (BWOA) and bear smell search algorithm (BSSA). BWOA accelerates the convergence speed with combination of the search strategy of BSSA; hence, it is named as improved black widow-bear smell search algorithm (IBWBSA) technique. Findings The multiple-objective operation denotes reducing generation cost, power loss, voltage deviation with optimally planning and operating the DS. For setting up the DG units on DS, IBWBSA technique is equipped to simultaneously reconfigure and find the optimal areas. Originality/value In this planning model, the constraints are power balance, obvious power flow limit, bus voltage, distribution substation’s capacity and cost. Then, proposed multiple-objective hybrid method to plan electrical distribution scheme is executed in the MATLAB/Simulink work site.

In the article the issues of taking into account of constraints in the form of inequality in optimal planning of short-term modes of power systems are considered. An efficient algorithm for solving of the problem is offered. On the basis of computational experiments for specific problems of optimal planning of modes of power systems the efficiency of the proposed algorithm is revealed.

Infrastructures for Electric Vehicle feeding, providing smart charging, vehicle-to-grid and local generation are of increasing interest. The operation of these systems and the integration with distribution networks can gain remarkable improvement by proper DC microgrid structure, able to reduce losses in converters, to improve controllability and to enhance overall availability. In this paper, a procedure for optimal planning of a DC microgrid for Electric Vehicle Supply Infrastructure is carried out, with specific tailoring according to technical configurations for source connection, involving different converter types and topologies. The procedure is completed by a reliability analysis, where rule-based curtailment is performed with multi-state matrix approach, and the effect of expected energy not supplied on total cost is evaluated, at different integration levels of energy storage. The proposed approach is applied to a case study of envisaged demonstrator for an Electric Vehicle service fleet.

Integration of distributed generation (DG) in distribution network changes the topological structure of distribution network. Meanwhile, the location and size of DG as well as the evolution of the topology structure of distribution network will affect the power supply reliability, which will make the planning process of the distribution network extremely complicated. For this, this paper presents a new distribution network planning model with reliability estimation method. The several network templates are obtained with the hierarchical clustering algorithm based on tree edit distance (TED), which could reduce the complexity of the distribution network. Then, the topology characteristics of the clustered network templates will be correlated with reliability index, which is formulated as explicit formulation. Similarly, the correlation model between the reliability index and DG planning is established through BP neural network. Furthermore, the distribution network planning model under each network template is aggregated, which contains the established reliability estimation model. Investment costs of distribution networks with different network templates are analyzed for different scenarios. The results suggest that unlike existing solution techniques for reliability correlated distribution network planning, the proposed method can effectively converge to the optimal solution, and evaluate the investment performance for large-scale distribution networks.

Aiming at the optimal configuration of distributed generations (DGs) and microgrids (MGs) of different operators in the distribution network (DN), this paper proposes a multiobjective joint optimal planning method that takes the benefits of DN-DGs-MGs into account. The operating principle of the distribution network aiming at the economics and power quality of the system and the economic benefits of DG operators and MG operators are used to build the joint optimization model. The installed capacities and access locations of distributed generations, and the interaction powers and configurations of microgrids are selected as the decision variables. A mathematical programming solver (Gurobi) combined with non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the joint optimization model. The effectiveness of the model and algorithm are verified in 33-bus system, and it is proved that a reasonable configuration through the joint optimization model can achieve the benefits of different operators and provide a reference for practical engineering applications.

Urban railways play a critical role in the daily life of citizens. However, extensive deterioration of mostly aged systems complicates the management of this type of infrastructure in copin...

The multi-energy system (MES) is believed to have bright prospects in the future for its advantages of high energy efficiency, flexible operation condition, and environmentally friendly. The heating network and the heat load play an important role in the MES and have great potentials in improving the system performance. In this paper, an optimal planning method is proposed for the MES that considers the thermal storage capacity of the heating network and the heat load. The objective includes the investment cost, the fuel cost, the grid cost, the maintenance cost, and the environmental cost. The constraints of the cogeneration, the heating network, and the heat load are all taken into consideration. The simulation based on the typical working conditions in annual operation is performed to verify the effectiveness of the proposed planning method. Four cases are set in order to comprehensively investigate the effect of the thermal storage capacity of the heating network and the heat load on the MES planning. The results indicate that the proposed method can decrease the capacity of the devices and reduce the fuel cost.

Existing research on on-grid microgrid planning is carried out with a free trading assumption and without considering the limitation of annual electricity exchange. Therefore, the existing planning and sizing scheme may be not viable for the application of partially self-sufficient microgrid (PSSMG) with a limited amount of electricity exchange. To address this issue, a new planning method for PSSMG is proposed in this paper considering the limited annual electricity exchanging amount (AEEA). The sizing model and energy management are linearized and simultaneously integrated into one model, which could be solved in polynomial time. In order to effectively reduce the number of variables of a full year horizon and to cope with the uncertainty both of DGs and loads, a data-driven method based on K-means algorithm is utilized to choose a set of typical days that are representative of historical data of one full year. Finally, the validity and effectiveness of the proposed model are verified by comparative numerical simulations, and the sensitivity of limited AEEA to the planning scheme is analyzed.

The demand side management (DSM) measures are designed to process a large number of controllable loads of different types in the multi-energy system, which is helpful to increase the sustainability of the overall energy system. This paper proposes a novel model for the optimal planning of integrated electricity-gas system (IEGS) with DSM that considers the impact of DSM measures. The proposed model minimizes the total cost of the system which includes investment cost, operation cost, carbon emission cost, and DSM compensation. In order to secure the normal operation of IEGS, the electricity and natural gas system normal operation constraints are included in the proposed model. By linearizing nonlinear equations, the formulation is transformed into a mixed-integer linear programming problem to enhance computational tractability. Finally, the combined IEEE 39-bus power system and 12-node natural gas system is adopted to verify the effectiveness of the proposed model for ensuring the system security.

As an important instrument for air-to-ground communication, the large space deployable antenna plays an important role in driving technological innovation and has become a topic of interest for research scholars worldwide. A large-scale deployable ring truss is introduced in this paper, which is equipped with a complete rope-driven driving method and supplements a cable net system in forming a complete space antenna. The antenna supporting arm is a slender structure that exhibits a certain flexible deformation during the deployment process of the antenna. A dynamic deployment model of supporting arms based on the absolute node coordinate formulation is established. In addition, based on the Lagrange equation, dynamic equations of multibody systems, including equations describing friction and flexible cable nets, are built. Furthermore, the optimal deployment process law of the antenna is developed. Finally, the performance of the deployment process of the antenna supporting arms and space deployable antenna is analyzed by the numerical simulation, which lays a solid foundation for subsequent prototype development and control theory research.

In smart distribution network, optimal planning of Energy Storage Systems (ESS) can lead to voltage profile improvement, network loss reduction and increasing the profit of distribution company (DISCO). This paper proposes the application of Modified Differential Evolution Algorithm (MDEA) which is very strong global search capability for maximizing the DISCO profit over the planning. The objective function is the difference between the benefit which is the energy arbitrage in a deregulated market and the costs which are investment cost, operation and maintenance cost. The implementation of MDEA is performed on a 30-bus radial distribution network. The simulation results demonstrate that the proposed MDEA application significantly increases the profit without any violation of network constraints.

In this study, an engineering economics software was developed to find the optimum design of renewable energy systems in micro-grid, off-grid, and on-grid cases. The software was used to calculate the renewable energy production from photovoltaic and wind turbines in local regions in Korea. TMY2 files were made for the renewable energy calculation based on meteorological data from 28 locations in Korea. To calculate the output of the photovoltaic and the wind turbine, a power generation model based on theoretical equations was developed. A fuel cell and diesel engine generator can be selected to use as a base power source model, and a battery energy storage device was included for efficient energy management. To verify the results of renewable power generations from solar and wind, the models were compared with commercial programs such as TRNSYS and HOMER. The results showed good agreement under the same conditions. A sensitivity analysis for a grid-connected factory in a specific region was carried out and the optimum investment design condition for a renewable energy system to reduce costs through peak load reduction was calculated. The sensitivity analysis for off-grid and grid-connected system designs was also performed and it will be continued to improve the accuracy of the model by comparing the data with that from a demonstration complex. The developed program can provide useful information for investment decisions through the optimal design of facility capacity and economic analysis when investing in renewable energy supply facilities.

Despite the great promise of hybrid solar-wind-biomass energy systems to power future sustainable cities, complexities associated with their optimal planning and design limit their wide-scale implementation. This study provides a novel systematic framework to identify optimal hybrid renewable solutions for urban areas at neighborhood scales. In particular, we examine the role of economies of scale in the techno-economic feasibility and environmental performance of hybrid renewable systems. For demonstration, we assess the impact of the economics of scale (at the neighborhood scales of 1/500, 1/250, and 1/100 of the city's electrical load) on the life-cycle costs of optimal hybrid renewable systems for Vancouver (Canada). Our results indicate that the total net present cost (NPC) of the optimized systems were 59, 116 and 290 million USD, while the levelized costs of electricity (COE) for the three studied scales were almost identical (0.300–0.307 USD/kW h). By comparing the proposed scenarios regarding gross atmospheric emissions, land requirements and economic performance, the mid-scale (1/250) with 6.3 MW of solar PV and 3 MW gasifier (~ 117 t/day biomass wastes) was preferable to the larger (1/100) and smaller (1/500) scale systems. Results from this study can help decision-makers in creating effective policies and mechanisms to advance the integration of hybrid renewable energy systems in cities.

An optimization model is formalized for the energy planning of urban districts equipped with renewable energy power plants (photovoltaic fields and wind micro-turbines), combined heat and power units, and traditional boilers; districts are connected to the public distribution grid. The main decision variables of the model are the number and typology of wind turbines and cogeneration plants, the size of photovoltaic fields and the power exchange with the public grid. The optimal values of the variables come from the minimization of installation, maintenance and operational costs, and satisfy load and technical constraints. The model is applied to three districts in the Savona Municipality that represent pilot sites of an extended project concerning the planning and management of smart urban areas. The analysis of different scenarios allows evaluating the role of uncertainties of renewable sources over the optimal solution of the planning problem.

Along with the rapid development of electric vehicle (EV) charging technologies, many new types of charging facilities have been utilized in electric vehicle charging stations (EVCSs). Charging facilities with different rated charging power can satisfy the charging demands from diverse EV owners, and simultaneously impact the spatial and temporal distribution of EV charging demands, which in consequence challenges the rationality and economy of EVCS allocation schemes. Based on this background, this paper indicates that EVCSs should be regarded to comprise multi-types of charging facilities during the planning stage, and a new optimization model is proposed for the target of minimizing the annualized social cost of whole EV charging system. To process the complexity of the optimization model, a two-step equivalence is proposed and applied. After the equivalence and some exact relaxation, the proposed optimization model has been transformed into the type of mixed integer second-order cone programming (MISOCP), which can be efficiently solved by appropriate mathematical methods. To demonstrate the feasibility and effectiveness of the proposed approach, a practical urban area fed by a 31-bus distribution system in China has been used as the test system and the numerical results are presented and analyzed.

We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning context.

This study presents an innovative design for the optimal sizing, placement, and dispatch approach of distribution static var compensators (D-SVCs) in a radial power distribution system to improve the technical and economic aspects of the grid. The approach incorporates the total harmonic distortion (THD) effects into the assessment with the presence of non-linear loads. A multi-state particle swarm optimisation algorithm is also proposed, at first to select the placement and size and then to select the dispatch strategy of D-SVCs. Three IEEE test systems were used for the case study to show the efficacy of the method. The results reveal that the approach is viable, and it determines the cases where the highest savings were achievable fulfilling the grid voltage and THD constraints.

Fast charging stations are critical infrastructures to enable a high penetration level of plug-in electric vehicles (PEVs) into future distribution networks. The fast charging stations need to be carefully planned to meet the PEV charging demand as well as reduce costs. This study addresses this technical challenge and proposes a hierarchical planning solution for both sitting and sizing of PEV fast charging stations based on a temporal-SoC (state-of-charge) characterisation and modelling of PEV fast charging demand. The optimal sitting of fast charging stations is firstly determined to ensure minimising the total number of stations and meeting the PEV fast charging demand considering the constraints of transportation networks and the expected PEV remaining mileage. Then the sizing (number of chargers and waiting spaces) of fast charging station is optimised by the use of M/M/s/N queuing model, so as to maximise the expected profit of the operator. The proposed solution is evaluated through a set of case studies for a range of scenarios, and numerical simulation results have confirmed the effectiveness of the proposed solution.

This paper presents a mathematical programming model for the optimal planning of an integrated system for producing fuels and biofuels considering the interaction with facilities capable of capturi...

This study proposes an integrated framework for planning distributed energy system with addressing the multi-objective optimization and multi-criteria evaluation issues simultaneously. The framework can be decomposed into two stages. At the optimization stage, the system design and dispatch are optimized considering multiple objectives by Ɛ-constraint method. Three decision making approaches are applied to identify the Pareto optimal solution. At the evaluation stage, a combined Analytic Hierarchy Process and Gray Relation Analysis method is proposed to evaluate and rank various optimal solutions when different objectives and cases are considered. Two stages of work are integrated by introducing the baseline conditions. As an illustrative example, an optimal planning model for a solar-assisted Solid Oxide Fuel Cell distributed energy system is proposed by Mixed Integer Non-linear Programming approach firstly. Then, the system is applied to different cases considering two types of buildings located in three climate zones. The obtained optimal solutions are further evaluated by the proposed multi-criteria evaluation method. Therefore, the overall optimal system design and dispatch strategy, as well as the best demonstration site can be identified comprehensively considering multiple objectives. In general, the results have verified the effectiveness of the proposed framework.

This work is devoted to the theoretical justification of a software and technological product that combines latest achievements in the field of mathematical computer modeling, sports physiology, and medicine. We present a review of theoretical foundations and practical solutions of the latest software and technological developments in the field of control over the training process in cyclic sports. We propose a new integrated approach to the training process planning, which combines known technology of the training impulse and a method for modeling individual physiological properties of the athlete's body on the basis of the physiological avatar technology.