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This article presents a detailed feasibility investigation of a Hybrid Renewable Energy System (HRES) designed to meet the energy needs of a university campus. The HRES includes Wind Turbine (WT), Photovoltaic (PV), Diesel Generator, Battery, and inverter components. Based on the constraint of power balance, different optimization techniques are applied to reduce the Annual Cost of the System and determine the optimum WT power, PV panel power, and number of batteries. An energy management strategy is presented in a way to minimize the Levelized Cost of Energy and Total Net Present Cost, and it is thought that the Loss of Power Supply Probability validates the reliability of the operation. To find the optimum sizing of the components, results are obtained using the HOMER and MATLAB software. The Genetic Algorithm (GA) outperforms during the simulation process, delivering quick and dependable results. Using GA in the best system configuration, 3.407975x103 kW PV, 50 kW WT and 951.5493 kW Battery, $3.9808 x105 annual system cost (ACS), $6.4580 x106 net present cost (NPC), and $0.1998/kWh, respectively. Solar panels cover the entire system, and the Renewable Energy Fraction (REF) is 100%. The results clearly show that the scheme that is proposed in this study can achieve a smooth flow of power using the identical optimal configuration., Bu makale, bir üniversite kampüsünün enerji ihtiyacını karşılamak üzere tasarlanmış bir Hibrit Yenilenebilir Enerji Sisteminin (HRES) detaylı bir fizibilite araştırmasını sunmaktadır. HRES, Rüzgar Türbini (WT), Fotovoltaik (PV), Dizel Jeneratör, Batarya ve invertör bileşenlerini içerir. Güç dengesi kısıtlamasına bağlı olarak, Sistemin Yıllık Maliyetini azaltmak ve optimum WT gücünü, PV panel gücünü ve pil sayısını belirlemek için farklı optimizasyon teknikleri uygulanır. Seviyelendirilmiş Enerji Maliyeti ve Toplam Net Bugünkü Maliyeti en aza indirecek şekilde bir enerji yönetimi stratejisi sunulmakta ve Güç Kaynağı Kaybı Olasılığının operasyonun güvenilirliğini doğruladığı düşünülmektedir. Bileşenlerin optimum boyutlandırılmasını bulmak için HOMER ve MATLAB yazılımı kullanılarak sonuçlar elde edilir. Genetik Algoritma (GA), simülasyon sürecinde daha iyi performans göstererek hızlı ve güvenilir sonuçlar sunar. GA'yı en iyi sistem konfigürasyonunda kullanmak, sırasıyla 3.407975x103 kW PV, 50 kW WT ve 951.5493 kW Batarya, 3.9808$ x105 yıllık sistem maliyeti (ACS), 6.4580$ x106 net mevcut maliyet (NPC) ve 0.1998$/kWh. Güneş panelleri tüm sistemi kaplar ve Yenilenebilir Enerji Fraksiyonu (REF) %100'dür. Sonuçlar, bu çalışmada önerilen şemanın, aynı optimal konfigürasyonu kullanarak düzgün bir güç akışı sağlayabileceğini açıkça göstermektedir.

Fil: Achkar, Victoria Gabriela. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Santa Fe. Instituto de Desarrollo Tecnologico para la Industria Quimica. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnologico para la Industria Quimica; Argentina

Coordinated charging can alter the profile of plug-in electric vehicle charging load and reduce the required amount of charging spots by encouraging customers to use charging spots at off-peak hours. Therefore, real-time coordinated charging should be considered at the planning stage. To enhance charging station’s utilization and save corresponding investment costs by incorporating coordinated charging, a new charging spot model, namely single output multiple cables charging spot (SOMC spot), is designed in this paper. A two-stage stochastic programming model is developed for planning a public parking lot charging station equipped with SOMC spots. The first stage of the programming model is planning of SOMC spots and its objective is to obtain an optimal configuration of the charging station to minimize the station’s equivalent annual costs, including investment and operation costs. The second stage of the programming model involves a probabilistic simulation procedure, in which coordinated charging is simulated, so that the influence of coordinated charging on the planning is considered. A case study of a residential parking lot charging station verifies the effectiveness of the proposed planning model. And the proposed coordinated charging for SOMC spots shows great potential in saving equivalent annual costs for providing charging services.

This paper presents a technique for the optimal planning and design of hybrid renewable energy systems for microgrid applications. The Distributed Energy Resources Customer Adoption Model (DER-CAM) is used to determine the optimal size and type of distributed energy resources (DERs) and their operating schedules for a sample utility distribution system. Using the DER-CAM results, an evaluation is performed to evaluate the electrical performance of the distribution circuit if the DERs selected by the DER-CAM optimization analyses are incorporated. Results of analyses regarding the economic benefits of utilizing the optimal locations for the selected DER within the system are also presented. The electrical network of the Brookhaven National Laboratory (BNL) campus is used to demonstrate the effectiveness of this approach. The results show that these technical and economic analyses of hybrid renewable energy systems are essential for the efficient utilization of renewable energy resources for microgrid applications.

Background: Low emission, high efficiency, and convenience of using plug-in electric vehicles (PEVs) are considered as significant benefits of PEVs. However, installation of parking lots (PLs) in o...

In the aftermath of natural disasters, optimal evacuation planning plays a crucial role in saving people’s lives. In this paper, we propose a mathematical model for optimal planning for helicopters with different capabilities to save victims with different urgency levels. The model is formulated as an optimization problem whose objective is maximizing the number of evacuees under operational constraints such as helicopter’s capacity and endurance and different urgency levels of victims. The proposed model is validated with an illustrative numerical example based on real-world data.

The global avocado market has increased significantly in recent years. The state of Michoacan in Mexico is one of the largest producers of avocado in the world. The ideal climatic conditions and favorable yields have spurred an interest in growing new orchards for enhancing avocado production. Such growth has a major impact on the environment due to the land use change and the substantial use of fresh water. Therefore, there is a need for an optimal strategic planning for the avocado production while accounting for the sustainable use of the available resources. This paper presents an optimization model for the optimal strategic planning of avocado production to satisfy the current and future demands while simultaneously accounting for the environmental impact (caused by land and water use) and economic factors. The proposed model is based on a new mathematical programming formulation. The results show that with proper planning, it is possible to satisfy the future demands for avocado while protecting the environment and achieving economic gains.

With the continuous strong supportive policies, distributed photovoltaic have developed rapidly in a high penetration in some regions, which requires grid reinforcement and is imposed to the curtailment risks. In response to the grid reinforcement costs, reduced losses, delayed grid investment and other costs or benefits associated with the access of high penetration distributed photovoltaic; this paper proposes a comprehensive cost-benefit analytical method in consideration of grid reinforcement. Analytical results of 8760 h power flow are acquired for optimized planning of the access of high penetration distributed photovoltaic. The research results may be valuable references for the scientific planning of high penetration distributed photovoltaic.

In this paper, a combined cooling heating and power micro-grid system framework which considers lots of device types and possible connections is presented. Based on this framework, a mathematical model considering constraints of load demand and operation is built; its optimal objective is to minimize the cost and carbon emission. The Multi-objective Particle Swarm Optimization (MPSO) method with a feedback loop is used to choose the best capacity of the equipment, and the Interior Point (IP) method algorithm is embedded in the MPSO to solve the operational optimization problem. The approach is applied to a mixed commercial and residential area of the Pearl River Delta (PRD) as a case study, verified the effectiveness of the planning method.

Electric power systems are facing major challenges because of the increase in penetration of energy resources (ERs). This paper focuses on composite system reliability based planning for ERs, and presents novel clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an optimal power flow (OPF) based approach in this paper. Once the optimal sizes and locations of ERs are obtained, the earliest year of penetration is determined using an adequacy check algorithm. Detailed studies presented considering the IEEE RTS demonstrate the applicability of the proposed technique.

With the rapid development of the global economy, the energy crisis and the deterioration of the ecosystem are becoming more serious. In this context, the development of electric vehicles has received the attention of all countries. Research on charging and changing facilities has a very important significance for the future comprehensive promotion of electric vehicles. In this study, the authors first analyse the advantages and disadvantages of different charging electric network modes. Then, they introduce distribution planning requirements of electric vehicles’ changing stations, and propose an optimisation model meeting various requirements when selecting station sites. This model aims to minimise overall construction and transportation costs, and meet the charging demand of drivers. Changing stations’ service types and operating characteristics of the substation act as constraint conditions. Finally, they solve the model based on the improved graph algorithms. It not only calculates the optimisation location of the changing stations, but also obtains the corresponding optimal substation access scheme. The research of this study can significantly guide planning and construction of electric vehicles’ charging and battery switch stations.

The need to solve problems of planning and justification of the rational level of centralization and concentration of sources' heat capacity was noted in the law “About a Heat Supply”. For the solution of these tasks, the complex methodology was developed; it allows us to define locations of heat sources and the border of their action at the predesign level of heat supply schemes' development of settlements. In presented complex methodology, standard values of heat load density per unit of the area (heat load density) and per unit of the pipelines' length (linear heat density) are criteria of scales restriction of the systems. The important task is to find their standard values. Authors offer dependences for determination of standard values of heat density indicators for carrying out a predesign analysis of heat supply systems. The analysis carried out showed that heat supply from chosen heat source of part of consumers isn't economically feasible if values of heat density indicators are less than their standard values. The less value of heat density in the system, the higher specific costs for generation, distribution and transmission of heat energy.

In traditional maintenance decision-making, maintenance planners assume that their prescribed Preventive Maintenance (PM) policies will be implemented without error. In practice, however, the individuals responsible for implementing such plans often deviate from the intended PM policy, resulting in unpunctual PM actions. We formulate cost-rate minimizing models to investigate the impact of such deviations, assuming that the actual PM time differs from the scheduled PM time in a probabilistic manner. We establish both analytical and numerical results for two specific types of maintenance policies, namely, age replacement with and without minimal repair.

This work addresses the optimal planning and campaign scheduling of biopharmaceutical manufacturing processes, considering multiple operational characteristics, such as the campaign schedule of batch and/or continuous process steps, multiple intermediate deliveries, sequence dependent changeovers operations, product storage restricted to shelf-life limitations, and the track-control of the production/campaign lots due to regulatory policies. A new mixed integer linear programing (MILP) model, based on a Resource Task Network (RTN) continuous time single-grid formulation, is developed to comprise the integration of all these features. The performance of the model features is discussed with the resolution of a set of industrial problems with different data sets and process layouts, demonstrating the wide application of the proposed formulation. It is also performed a comparison with a related literature model, showing the advantages of the continuous-time approach and the generality of our model for the optimal production management of biopharmaceutical processes.

This paper presents the optimal planning of the Nordic backbone transmission system with 100% electric vehicle penetration of passenger cars by 2050. Electric vehicles will play an important role in the future energy systems and can reduce the greenhouse gas emission from the transport sector. However, the electric vehicles will increase the electricity consumption and might induce congestions in the transmission systems. In order to deal with the electricity consumption increase from the electric vehicle integration into the power system and maximize the social welfare, the optimal investments of the Nordic transmission system are studied. Case studies were conducted using the market simulation model EMPS (Efi's multi-area power market simulator) and two electric vehicle charging scenarios: a spot price based scenario and a dumb charging scenario. The electric vehicle charging power is assumed to be 3.68 kW with 1 phase 16 A. The complete electrification of the private passenger fleet increases the yearly power demand in the Nordic region with ca 7.5%. The profitable increases in transmission capacities are highest for dumb charging, but are very low for both dumb and spot price based charging compared to a Reference case.

Recently, electric vehicles (EVs) have been seen as a felicitous option towards a less carbon-intensive road transport. The key issue in this system is recharging the EV batteries before they are exhausted. Thus, charging stations (CSs) should be carefully located to make sure EV users can access a CS within their driving range. Considering geographic information and traffic density, this paper proposes an optimization overture for optimal siting and sizing of a rapid CS (RCS). It aims to minimize the daily total cost (which includes the cost of substation energy loss, traveling cost of EVs to the CS, and investment, variable, and operational costs of the stations simultaneously) while maintaining system constraints. The binary gravitational search algorithm, genetic algorithm, and binary particle swarm optimization algorithm were employed to optimize the daily total cost by finding the best location and sizing of the RCS in a given metropolitan area in Malaysia. The results show that the proposed methods can find optimal locations and sizing of a RCS that can benefit EV users, CS developers, and the power grid.

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

It is of great significance to study the optimization of grid structure considering comprehensive maintenance. In this paper, a mathematical model of optimal planning for comprehensive maintenance of power grid was established. The model can consider various technical measures to meet the comprehensive maintenance of power grid, and consider the operation status of power grid. An optimization model of power grid integrated maintenance was built, and an optimization model based on Tabu search was proposed. The above theory and method are applied to the optimal planning of Chaoyang Power Grid, which is a typical city in Liaoning, and the validity and practicability of the method are verified.

This paper proposes a decision model for the planning of decentralized energy production in urban areas. The approach aims at finding the optimal mix and sizing of the generation units to be installed in a number of sites, given the characteristics of each location in terms of radiation, wind potential, electrical and thermal loads, etc. Both dispatchable and renewable (non-dispatchable) energy sources are taken into account; in particular, in the present application the sources considered for installation include cogeneration microturbines, photovoltaic plants and wind turbines. As both renewable sources and loads are stochastic in nature, a proper approach is used to evaluate the effects of the related uncertainties. The proposed model is applied to a neighborhood in Savona, Italy. The goal of the technique is to represent a tool for a decision maker when planning investments in different urban areas, in the context of the transition from a traditional city to a "smart" one.

This paper proposes an approach to optimal planning under uncertainty with limited sensory information for an autonomous unmanned vehicle (UXV). We consider a surveillance application in which identification of environmental targets is impacted by controllable features commanded by the UXV as well as ambient features with dynamics that are uncontrollable. To manage computational complexity, mission states are defined by abstract or discretized features that are maximally influential based on Shannon information content. A receding horizon optimization method is applied to find optimal actions given uncertain and potentially erroneous sensor readings. Ambient feature transition probabilities are learned from empirical data then integrated with controllable features that evolve as a function of UXV actions.

This study was carried out to analyze water suspension in the water supply system through fault tree analysis. And quantitative factors was evaluated to minimize water suspension. Consequently the aim of this study is to build optimal planning by analyzing scenarios for water suspension. Accordingly the fault tree model makes it possible to estimate risks for water suspension, current risks is . The result of scenario analysis by pipe replacement, risks for water suspension was reduced when replacing WD4 pipe. As a result of scenario analysis by water district connections, the amount of risk reduction is maximized when it is connecting to network pipe of D Zone. Therefore, connecting to network pipe for D Zone would be optimal to reduce risk for water suspension.

This paper presents a new multi-stage model, based on the mixed integer nonlinear programming (MINLP) approach, to determine the optimal sub-transmission system expansion planning (SSEP). This model considers the placement of distributed generation (DG) units in distribution networks over the planning periods. The SSEP determines the facilities which should be installed and/or reinforced so that the system serves the forecasted demand at the lowest cost while satisfying the operational constraints. These constraints include the maximum allowable voltage drop, and the operation capacity of substations, MV feeders, and DG units. Meanwhile, the proposed model presents the optimal planning of existing and new sub-transmission substations, as well as the routes of medium voltage (MV) feeders. Moreover, the allocation and operation planning of DG units over the planning period is considered, as well. On the other hand, the expanding limitations of existing and candidate sub-transmission substations and DG units are considered as the expansion constraints in the SSEP problem. The effectiveness of proposed MINLP-based model is demonstrated using several case studies.

Planning of charging stations for electric vehicles (EVs) is the foundation of EV’s promotion and development. A novel multi-objective optimization model is proposed in this paper to better locate and size the charging stations for EVs from given alternative sites. Economic costs of charging station and the time wasted in waiting for charging are minimized in the model. The capacity constraints of the charging stations are included as main constraints. Pareto front is used to compare and estimate the practical value of the current substation locating and sizing plans. A calculation method based on rasterization and Voronoi diagram is proposed to make it clear where the jurisdiction of each charging station ends. Finally, the proposed model and calculation method are verified using case study.

Heating/cooling systems have played an important role in building energy and comfort management. There have been intensive discussions about the integration of heating/cooling systems into the smart grid infrastructure over the past decade, yet controlling the operation of heating/cooling systems in a smart grid with high penetration of renewable resources has not been addressed clearly. This study has investigated the suitability of a novel active controller applied to heating/cooling systems in the context of smart grid with high penetration of renewable energies. The proposed controller operates by responding to a combination of internal set points and external signals (e.g. the availability of renewable energy resources and welfare of customers) from a single local controller. The heating/cooling systems management minimizes the overall cost of the simulated smart microgrid, the size of smart microgrid units, and the imported energy from the distribution grid through an optimization process. It also at the same time maximizes the reliability of the smart microgrid. Demonstrated results confirm the capability of the proposed heating/cooling system controller on the planning of a smart microgrid.

The problem of generalized power sources planning in distribution system is researched. Meanwhile, the active and reactive power of generalized power source are treated as control variables. In this paper a generalized index for probabilistic optimal planning is presented. Based on this, a new probabilistic assessment model being setted up, a new glowworm swarm optimization algorithm is used to optimize the generalized power source. Finally, the model and algorithm are tested and improved by modified IEEE 33-node example. The calculation result shows that the proposed model and algorithm are feasible and superior.

The approaches and algorithms proposed in the present study form the basis of a novel technology for identifying heat systems. This technology consists in actively influencing the conditions that determine the accuracy of estimation of the actual parameters of real systems whose knowledge renders it possible to solve efficiently the problems of modernization, checkout, and centralized control. The method presupposes a sequential planning strategy: each experiment is conducted with due consideration of the data obtained after the results of the preceding one are processed. The procedure of planning the test environment, positioning the measuring equipment, conducting the experiment, and processing and analyzing its results is repeated at each step in the proposed method.

This paper proposes a new method for the planning of stand-alone microgrids. By means of clustering techniques, possible operating scenarios are obtained considering the daily patterns of wind and load profiles. Then, an approximate analytical model for reliability evaluation of battery energy storage system is developed in terms of the diverse scenarios, along with multistate models for wind energy system and diesel generating system. An optimal planning model is further illustrated based on the scenarios and the reliability models, with the objective of minimizing the present values of the costs occurring within the project lifetime, and with the constraints of system operation and reliability. Finally, a typical stand-alone microgrid is studied to verify the efficiency of the proposed method.

The task considered in the article is a constituent part of the problem of identifying pipeline systems, central to which is exerting an active effect on the conditions determining the accuracy with which the actual parameters of real systems are estimated, the knowledge of which is of key importance for efficient retrofitting, adjustment, and dispatch control. An algorithm invariant to possible problem statements is developed, which allows the global solution to be obtained within a finite and limited number of steps.

This paper proposes an integrated model for making a group of strategic decisions about oil and gas development projects simultaneously over a long-term planning horizon. These decisions involve: selection of field and pipeline development projects, scheduling of selected projects, production planning, and upstream transmission planning. The proposed model is formulated as a linear mixed-integer-programming model. It is implemented in a case study to demonstrate its usefulness and applicability in practice. Finally, a number of sensitivity analyses are carried out to analyze the impact of most influential uncertainties on the solutions and the corresponding results are discussed.

As a promising transport in the future, electric vehicles plays an important role in people's lives and energy conservation. Planning of electric vehicle charging stations has a far-reaching significance for the popularity of electric vehicles. In this paper, we discuss the siting problem of electric vehicle charging station and propose a two-step method of optimization method. Firstly, we establish a charging station location model, then use Voronoi diagram to determine the preliminary zone, finally we get this problem optimally solved by immune algorithm.The example verifies feasibility of this model.

SUMMARYThis paper proposes a new technique based on a Parameter-Optimal Iterative Learning Control (POILC) to track a command pitch rate of a high-speed supercavitating vehicle (HSSV). The pitch rate of a supercavitating vehicle has non-minimum phase behavior. Thus, tracking is fundamentally limited to poor performance. To solve this problem, a feed-forward control can be used while using the cavitator as a control input in the feed-forward path to modify the slow response caused by non-minimum phase behavior. The main idea of this paper is to apply the cavitator input with high precision as a feed-forward control to improve tracking performance. The exact value of the feed-forward control is achieved using POILC. However, in the presence of uncertainty, zero convergence of POILC algorithms is threatened. It will be shown that applying adaptive weight in the performance index, the convergence is guaranteed in the presence of uncertainty and also when the system is sign-indefinite. The proposed technique includes an optimal planning to make the error norm monotonically convergent to zero. The convergence and perfect tracking will be guaranteed through a Lyapunov candidate. Performance and significance of the proposed supercavitating vehicle control will be verified by simulation.

This article considers a system with a deterministic initial lifetime that generates reward at a decreasing rate as its virtual age increases. Maintenance can be performed to reduce the virtual age of the system; however, maintenance also shortens the remaining lifetime of the system. Given this tradeoff, the lifetime reward-maximizing maintenance policies under perfect maintenance for non-failure-prone systems, and both perfect and imperfect maintenance for failure-prone systems are analyzed. For each combination considered, structural properties of the resulting optimal policies are derived and exploited to develop solution techniques. Insightful numerical examples are also provided.

This paper solves the distributed generation (DG) planning problem. Firstly, multiple-objective functions have been formed with the consideration of minimum line loss, minimum voltage deviation and maximal voltage stability margin. Secondly, the proposed improved NSGA-II algorithm has been described in detail to solve the multi-objective planning problem. An improved Non-dominated Sorting Genetic Algorithm II has been proposed for optimal planning of multiple DG units in this paper. Experiment has been made on the IEEE 33-bus test case with the consideration of multiple DG units. The computational result and comparison indicate the proposed algorithm for optimal placement and sizing of DG in distribution system is effective.

Recently, distributed generation (DG) has gained lots of attention due to a variety of benefits it can bring to the traditional power produce and distribution system. Identify the optimal location and size of DG in the distribution network is one of the crucial problems of DG integration, because a non-optimal planning might cause some adverse effects. In this paper, an optimization model with the consideration of minimizing energy losses is formulated first, and then an optimization methodology based on the Self-organizing Optimization Algorithm (SOA) is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed procedure.

An approach is presented for the estimation of minimum PEV charging infrastructure needs, and the optimization of infrastructure deployment for highway corridors. We utilize continuous facility location models, which is distinguished from previous research that uses graph theory methods. We aim to obtain the optimal locations of publicly-funded infrastructure, to complement infrastructure provided by private industry, while carefully dealing with demand uncertainty. A case study is presented for highway corridors in the Texas Triangle megaregion. A discussion is provided on demand uncertainty and implementation considerations as well.

In this study, a probabilistic planning approach is proposed for optimally allocating different types of distributed generator (DG) (i.e. wind-based DG, solar DG and non-renewable DG) into a harmonic polluted distribution system so as to minimise the annual energy losses and reduce the harmonic distortions. The proposed planning methodology takes into consideration the intermittent nature of the renewable resources, load profile and the technical constraints of the system. The objective function is the total system annual power loss. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, total harmonic distortion (THD) limits and maximum penetration limit of DG units. The optimisation process is achieved using the genetic algorithm optimisation method. This proposed approach has been applied to a typical rural distribution system with different scenarios including all possible combinations of distributed energy resources. The simulation results using Matlab programming environment show that significant reductions in the energy losses and THD are achieved for all the proposed scenarios. Also simulation results depict that the proposed method is robust and computationally efficient.

In order to determine the layout of electric car charging stations, a model for optimizing charging stations location is developed after charging-demand districts are divided, the number of electric vehicles and the center of each charging district are ready. This model takes the minimization of electric vehicles charging stations total cost which includes initial fixed investment costs, operating costs and charging costs as the objective function, some related constraints which include service radius, capacity of charging station etc. are considered. Particle swarm optimization based on hybridization is proposed to solve this problem. The example verifies feasibility of this method.

This paper established a transformer substation comprehensive optimal planning model considering distribution generation (DG) and different block geographic information factors (GIF), set form, volume, location of existing DG in planning area and transformer substation load-bearing capacity as constraint condition, taking construction cost of distribution transform substation and feeder and operation cost including current supply loss into account, in the meantime, regarding the influence of GIF such as land properties and so on to location and cost of construction with load demand satisfied. Furthermore, influence factors of different block information factor to construction cost were work out by means of interval analytical hierarchy process. On the basis of the established objective function, an particle swarm optimization (PSO) algorithm is proposed to solve the problem in this paper. By empirical study of certain planning area, the proposed model and algorithm are proved to be scientific and effective.

The aim was to investigate the influence of raw material variation in Atlantic salmon from aquaculture on filleting yield, and to develop a decision tool for choosing the appropriate raw material for optimized yield. This was achieved by tracking salmon on an individual level (n = 60) through a primary production site. The majority of the salmon exhibited a heavier right fillet compared to the left fillet after filleting. No explicit explanation was found for this observation although the heading procedure was shown to have a large impact. A Partial Least Square model was built to predict the yield after filleting. The model was based on six pre-processing variables and allowed an acceptable prediction of the filleting yield with a root mean square error cross validation of 0.68. The presented model can estimate the slaughter yield for a certain batch before ordering from the slaughterhouse. This may facilitate optimal planning of the production of salmon fillets by ordering and assigning the right batch to the right product category to obtain an optimal yield and quality.

Optimal planning of a cogeneration plant operation for a day-ahead horizon with emphasis on provision of ancillary services is presented in this paper. A first-principle mathematical model is used to formulate an optimization problem in the form of a mixed integer and linear program (MILP). The resulting decision support tool recommends the plant's operator the best strategy how to trade electricity, provide contracted ancillary services and operate the plant in each hour in order to maximize profit. Thanks to the cooperation with the company Taures, a.s., this tool has been in operation at two large cogeneration plants for more than two years.

This paper presents a methodology for substation optimal planning considering DG for peak shaving. Utility can take effective demand-side management (DSM) to encourage customer-owned DG to participate in peak load shaving, and it can also construct utility DG to meet the peak load demand. In this paper, the impact of DG on peak load shaving is analyzed, and DG is taken as a complement to T&D system to meet load demand, which is considered in the substation planning. Substations sizing and location and new-built utility DG capacity is optimized using Particle Swarm Optimization (PSO), in which supply area of each substation is obtained by Voronoi diagram method. Case study shows that planning result considering DG for peak shaving can defer T&D system expansion so that considerable investment can be saved. Especially for those areas with high cost of T&D system construction, constructing DG to meet peak load demand would be a more economic way.

Construction of electric vehicle charging station is the premise of the popularity of electric vehicles. In this paper, electric vehicle charging facilities planning is divided into two stages: public demonstration stage and commercial operation stage. In public demonstration stage, This Paper applies maximal covering model into charging station location, and then be solved by the branch and bound method. In commercial operation stage, the charging station’s service areas division is studied based on Voronoi diagram. With the concerning technical restrains, an optimal planning method is presented for newly-increased station positioning and service region division. The method using the most greatly air circuit method and local dynamic characteristics of Voronoi diagram guarantees the reasonable distribution of stations. Then charging station model of optimal load allocation is established by using M/M/c model. In the end, the correctness and validity of the model is verified through simulation of charging station planning.

To be able to reach objectives such as peak shaving or self-consumption within smart grids, it becomes increasingly important to control smart grid ready devices within households. This paper explores the planning of devices with minimal run-time constraints and constraints on total cumulative production (e.g., buffer level), such as heat pumps and electric vehicles. For such devices, a novel dynamic programming formulation is presented that makes it possible to find the optimal planning in $\mathcal{O}(N^2)$ time and space, where $N$ is the number of time intervals. To evaluate the algorithm, we integrate it into an existing decentralized demand side management approach. This evaluation shows using simulations that a load curve within a neighborhood can be flattened this way.

During the individual time-trial competitions in cycling, the cyclist’s skills are essential, but it is also important the race strategy. The race strategy includes the manner in which the cyclist competes and the selection and set-up of the cycling equipment. For hilly time-trials, part of the race strategy consists of the right selection of the bike type. Depending on the characteristics of the road such as total distance and altitude profile, a time-trial bike or a traditional road bike could be used. Additionally, in some races it is possible to change bike type as a part of the strategy. This strategy seeks to take advantage of time-trial bikes during low gradient sections and to take advantage of road bikes during high gradient sections. The purpose of this work is to plan an optimal bike change strategy to determine if it is advantageous to change the bike type, and if so, to find the point of the route where the change minimizes race time. The optimal planning methodology is based on a bike model, a simplified altitude profile and an optimization problem. A model parameters identification process is performed based on experimental tests in a hilly route. This route is used as a case study for the optimal planning of the bike change strategy.

With the rapid development of economic and society, the planning and construction of distribution network mainly focus on the improving of power supply capacity and power supply reliability, while there exists commonly a lack of attention to the operation efficiency and benefits. The existing research about the efficiency of the power industry mainly concentrated in the power generation link, lack of analysis for the distribution. Based on the foundation of the operation efficiency and profit connotation, this article proposed an evaluation index system to reflect the efficiency and profit of the distributed network. Based on the comprehensive evaluation index of operation efficiency and benefits, weestablished a planning optimization model for thetransformer substations, and the goals of the model was to promote the efficiency and benefits of the transformer substations. The optimal planning constraints were also determined. We selected Voronoi and differential evolution algorithms to solve the optimization model and put forward the overall process of optimal planning problem. Through the application in typical pilot area, the validity and the practicability of the method were verified.

This paper introduces mathematical modelling, the development of diagnostic methods and the design of producing a prototype. Its aim is to establish a new, accredited research laboratory for the testing and quality assurance of hybrid and electric driven vehicles. The task is very complex and it puts special emphasis on environmental and safety issues. It highlights the fact that its application under laboratory conditions establishes a new situation for measurement. It points out that the complex research which develops diagnostic techniques and procedures related to real operation and urban environment, moreover, applies optimal planning methods still has novelty.

Feeder system is an important part of the distribution system, which is not only shouldering the mission of fulfilling social responsibility, but also related to economic benefit of power supply companies. In the traditional planning method of feeder system, power supply reliability is implicitly reached by “N-1” criterion, but it is not particularly effective in reducing costs and handling budgets. Therefore, this planning method does not meet distribution network differentiation and meticulous management requirements and it's difficult to protect the interests of power supply companies and consumers, unable to adapt to future development needs of distribution network. In this paper, based on the different requirements for power supply reliability in different regions, the key elements of feeder system (feeder capacity, zone, segmentation, protection, switching time, etc.) and their flexible configuration method were proposed. Based on coordination accident support capacity of different zone switching mode and transfer capability for feeder system, feeder system planning method that supports feeder layer outage, transformer outage and substation outage and the overall process of feeder system reliability design were put forward, to ensure planning scheme achieve satisfactory electrical performance, accident control, optimal power supply reliability and economy. Finally, distribution network optimal planning in high reliability demonstration area of a city was selected as an actual case and satisfied planning scheme was obtained, to verify the effectiveness of the proposed planning methods.