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The inevitable voltage sag events cause losses to distributed generation (DG) and sensitive users. Reasonable planning for both of them helps to mitigate the impact of voltage sag. In this paper, a joint optimal planning method is proposed for DG and large sensitive user (LSU) considering the influence of voltage sag. Firstly, the new models of accelerated trip and chain trip for DG under voltage sag are established, to characterize the influence of DG tripping on other DG and sag characteristics during the sag event accurately. Secondly, a new voltage sag frequency assessment method is proposed, which considers models of accelerated trip and chain trip for DG, the tripping characteristics of distance protection (DP), to quantify the voltage sag severity accurately of DGs and LSUs in the gird which is with DP. Lastly, a joint optimal planning model is established considering the losses of DG and LSU under voltage sag, to support the planning of DG and LSU in different scenarios. The method is applied to IEEE RTS-30 for verification, proving that the method can give a direction of the most beneficial solution for the planner, to reduce the losses of voltage sag to both the power system and users.

Renewable energy sources have recently been integrated into microgrids that are in turn connected to electric vehicle (EV) charging stations. In this regard, the optimal planning of microgrids is challenging with such uncertain generation and stochastic charging/discharging EV models. To achieve such ambitious goals, the best sites and sizes of photovoltaic and wind energy units in microgrids with EV are accurately determined in this work using an optimization technique. This proposed technique considers 1) generation profile uncertainty in photovoltaic and wind energy units as well as the total load demand, 2) photovoltaic and wind generation units' DSTATCOM operation capability, and 3) various branch and node constraints in the microgrid. Most importantly, the possible EV requirements are also taken into account, including initial and predetermined state of charge (SOC) arrangements, arrival and departure hours, and diverse regulated and unregulated charging strategies. A bi-level metaheuristic-based solution is established to address this complex planning model. The outer level and inner-level functions optimize renewable energy sources and EV decision variables. Sub-objectives to be optimized voltage deviations as well as grid power. The results demonstrate the effectiveness of the introduced method for planning renewable energy sources and managing EV to effectively achieve autonomous microgrids.

This paper presents a practical optimal planning of solar photovoltaic (SPV) and battery storage system (BSS) for electric vehicle (EV) owner households with time of use (TOU) electricity pricing. The main aim of the optimisation problem is to minimize the Cost of Electricity (COE) while satisfying the design constraints over 20‐year project lifespan. Novel rule‐based home energy management systems are created to investigate the optimal sizing of two system configurations: (1) SPV‐EV, and (2) SPV‐BSS‐EV. The arrival and departure times of the EV's availability, as well as its initial state‐of‐charge are incorporated into the energy management system via stochastic functions. Sensitivity analyses of the feed in tariff, grid constraint, electricity demand, and available rooftop area are provided to investigate the variations of cost of electricity and capacities of SPV and BSS. From the study results, practical guidelines for grid‐connected households, based on TOU energy pricing, are provided to select the optimal capacity of SPV and BSS to accommodate the existing EV. The optimisation method is general and applicable to any household owning EV; however, in this study realistic data of solar insolation and temperature as well as household electricity demand and electricity prices are implemented for South Australia. Uncertainty analysis is investigated based on 10‐year real and stochastic data to prove the optimal results. It is found that the optimal configuration for a typical SA household utilising TOU pricing with an EV requires a 10‐kW SPV with a 10‐kWh capacity of BSS.

Federal energy regulatory commission (FERC) Order #2222 prescribes that distributed energy resources (DERs) with 100 kW or more capacity in aggregate should be allowed to participate in organized electricity markets. Most aggregation is via a combination of disparate DER technologies such as solar, wind, storage, electric vehicles, and smart load units. Another stumbling block to enabling participation of DERs in organized electricity markets is the energy limitation. However, there is a lack of aggregator models in the literature that gainfully allow aggregation of disparate DER technologies that are energy limited. To address this shortcoming, we proposed a disparate DER aggregator (DDA) planning model here, that overcomes energy limitation of DERs. The DDA planning model considers multiple revenue streams of (1) capacity credits; (2) energy revenues; and (3) ancillary services revenues. The proposed DDA planning model enables disparate DER technologies to collate and provide a firm power capacity and participate in the market capacity auction and receive capacity credits. This comprehensive DDA planning model considers the dynamic/temporary aggregations with other facilities through peer‐to‐peer (P2P) trade, and maximization of the net present value (NPV) revenues over the planning horizon. The developed model is tested on sample and practical large‐scale case studies. Additional sensitivity analyses are performed, demonstrating the favourable performance and the business potential of the developed DDA planning model.

Numerous investigations have been carried out to evaluate microgrid optimal configuration. The main goal of this research is evaluating the positive effect of private investor that contributes in t...

In order to address the increased demand for utility with the adoption of electrification in the transport sector, infrastructure development plays an important role. This research helps in planning the optimum utilisation of the existing infrastructure. The planning of electric vehicle (EV) infrastructure is analysed by considering various loads such as (1) static (2) dynamic and (3) heuristic on balanced radial distribution system (BRDS). The introduction of electric vehicle charging station (EVCS) into BRDS increases the total losses. The optimised reconfiguration helps in reducing the losses and ensures increased reliability of the system. The major objective of this process is to minimise both the real power loss and the reliability index energy not supplied of the radial distribution system (RDS) with the placement of charging stations. The planning is accomplished with a single‐step process in which the optimum location of the EVCS along with reconfiguration of BRDS is done using the multiple objective particle swarm optimisation (MOPSO). The performance of the BRDS with the proposed method is elaborated under three scenarios with three internal cases. The proposed methodology is validated through simulation for a three feeder IEEE 16 bus BRDS considering various load scenarios using MATLAB.

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.

North African countries generally have strategic demands for energy transformation and sustainable development. Renewable energy development is important to achieve this goal. Considering three typical types of renewable energies— wind, photovoltaic (PV), and concentrating solar power (CSP)—an optimal planning model is established to minimize construction costs and power curtailment losses. The levelized cost of electricity is used as an index for assessing economic feasibility. In this study, wind and PV, wind / PV / CSP, and transnational interconnection modes are designed for Morocco, Egypt, and Tunisia. The installed capacities of renewable energy power generation are planned through the time sequence production simulation method for each country. The results show that renewable energy combined with power generation, including the CSP mode, can improve reliability of the power supply and reduce the power curtailment rate. The transnational interconnection mode can help realize mutual benefits of renewable energy power, while the apportionment of electricity prices and trading mechanisms are very important and are related to economic feasibility; thus, this mode is important for the future development of renewable energy in North Africa.

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.

The penetration of e-mobility is growing thanks to the European guidelines on climate preservation regarding the reduction in CO2 emission. Governments are adapting their economic policies with the aim to incentivize e-mobility. At the same time, with a view to equality and accessibility, countries are working to introduce e-mobility services also for people with disabilities in order to improve the quality of their lives. The paper reports the deployment of an e-mobility service for persons with disabilities carried out in a project financed by Sapienza University of Rome. The project includes a feasibility study and a cost–benefit analysis in order to identify the optimal solution from a technical and environmental point of view for a sustainable e-mobility service for people with reduced mobility. A methodology to design a service based on optimal routes and electric vehicles with respect to energy consumption, time travel, energy and vehicle costs and quality of service is proposed. The 5-step methodology calculates the most energy-efficient routes and defines the optimal charging schedule, taking into account charging points dislocated along the routes and choosing vehicle typologies with the best performance based on economic evaluations. A software was developed to automatize the methodology.

Value chain collaboration management is an effective means for enterprises to reduce costs and increase efficiency to enhance competitiveness. Vertical and horizontal collaboration have received much attention, but the current collaboration model combining the two is weak in terms of task assignment and node collaboration constraints in the whole production-distribution process. Therefore, in the enterprise dynamic alliance, this paper models the MVC (multi-value-chain) collaboration process for the optimization needs of the MVC collaboration network in production-distribution and other aspects. Then a MVC collaboration network optimization model is constructed with the lowest total production-distribution cost as the optimization objective and with the delivery cycle and task quantity as the constraints. For the high-dimensional characteristics of the decision space in the multi-task, multi-production end, multi-distribution end, and multi-level inventory production-distribution scenario, a genetic algorithm is used to solve the MVC collaboration network optimization model and solve the problem of difficult collaboration of MVC collaboration network nodes by adjusting the constraints among genes. In view of the multi-level characteristics of the production-distribution scenario, two chromosome coding methods are proposed: staged coding and integrated coding. Moreover, an algorithm ERGA (enhanced roulette genetic algorithm) is proposed with enhanced elite retention based on a SGA (simple genetic algorithm). The comparative experiment results of SGA, SEGA (strengthen elitist genetic algorithm), ERGA, and the analysis of the population evolution process show that ERGA is superior to SGA and SEGA in terms of time cost and optimization results through the reasonable combination of coding methods and selection operators. Furthermore, ERGA has higher generality and can be adapted to solve MVC collaboration network optimization models in different production-distribution environments.

Cross-border grid interconnection is a critical means to achieve wide-area share of hydro and other clean energy. Economic benefit assessment of cross-border grid interconnection projects should be carefully performed during early stage. In this paper, a method based on cost-benefit economic assessment for optimal planning of cross-border grid interconnection is proposed. An economic index for comprehensively assessing the cost of a transmission project and its resulting benefits of more usage of hydro energy is designed first. A chronological production cost simulation model considering hydro energy spillage due to transmission congestion and thermal operational limitation is then proposed to calculate the economic index. A case study is performed using the proposed method to determine the optimal capacity of a potential transmission link between Brazil and Argentina, which have rich and complementary hydro energy resources. Keywords: Grid interconnection, Transmission planning, Hydropower generation, Economic assessment

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.

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

Optimal planning of a remote area electricity supply (RAES) system is a vital challenge to achieve a reliable, clean, and cost-effective system. Various components like diesel generators, renewable energy sources, and energy storage systems are used for RAES systems. Due to the different characteristics and economic features of each component, optimal planning of RAES systems is a challengeable task. This paper presents an overview of the optimal planning procedure for RAES systems by considering the important components, parameters, methods, and data. A timely review on the state of the art is presented and the applied objective functions, design constraints, system components, and optimization algorithms are specified for the existing studies. The existing challenges for RAES systems’ planning are recognized and discussed. Recent trends and developments on the planning problem are explained in detail. Eventually, this review paper gives recommendations for future research to explore the optimal planning of components in RAES systems.

In view of the practical application requirements for the rapid expansion of electric taxis (ETs) and the reasonable planning of charging stations, this paper presents a method for mining latent semantic correlation of large data by the trajectory of ETs and the planning of charging stations with optimal cost. Firstly, the vector space modeling method of ET trajectory data is studied, and the semantic similarity of the trajectory data matrix is evaluated. Secondly, the hidden characteristics of the mass trajectory data are extracted by matrix decomposition. Then, the latent semantic correlation characteristics of trajectory data are mined. Finally, the fast clustering of ETs is realized by the spectral clustering method. On this basis, with the objective of minimizing the annual construction and maintenance costs of charging stations, the optimal planning scheme of charging stations for ETs is given. In this paper, the spectrum clustering processing method of the potential semantic correlation of the big data of the driving track of ETs can be combined with the operation and maintenance costs of the charging station, and the convenience of charging for ET users is also considered. This provides decision support information for the reasonable planning of charging stations.

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.

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.

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.

When solving a given common task, a swarm of moving objects coordinates the state of its individual members. When planning swarm operations, there is a need to take into account the possibility of its operational regrouping, since at the time of planning the exact purpose of the swarm operation may be either not yet defined, or secret, or determined by a number of random circumstances. At the same time, the swarm resources are insufficient for one-time coverage of all possible targets in a given service area. Therefore, the execution of the operation of the swarm begins to resolve the aforementioned uncertainties. In this case, the operation time can be significantly reduced. This problem is solved by methods of the percolation theory. A concept of programmable percolation of the service area, which is implemented in two phases, is introduced. The value of the concentration of objects in a basic stochastic swarm is obtained numerically -- using the results of statistical modeling of two-phase operations -- and analytically, providing a minimum total cost of the two-phase operation. The synergy of information interaction between the swarm objects when implementing a programmable percolation path is analyzed.

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 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.

Voltage dip and long duration interruption (LDI) are among the most costly power quality phenomena. In this study, a Monte-Carlo based approach is proposed to consider the cost of sensitive loads disruption caused by dip and LDI in the optimal planning of synchronous distributed generations (SDGs). The idea is to link between trip probability due to dip and LDI and their yearly costs by employing Monte-Carlo simulation and acquiring their total costs during the planning horizon. The addition of disruption cost along with the traditional planning objectives like network upgrade cost and loss cost allows utilities to include the customer's perception during planning. A formula for the probability of disruption due to dip is derived and a modified Monte-Carlo approach is proposed. The methodology is illustrated on the distribution level of the IEEE 30-bus system and the optimisation problem is solved by particle swarm optimisation algorithm. The results demonstrate that the sensitive loads performance is improved from the dip standpoint in the presence of SDGs. However, the LDI cost is either not affected or aggravated by the presence of SDGs depending on the protection model. The total disruption cost is decreased.

The paper suggests analysis methods for optimal planning models in oil refining. The basic trends of optimization models construction are described. It is shown that parametric analysis is vital for improving the efficiency of activity. Finally, the economic efficiency of the suggested methods is estimated.

Wireless sensor network deployments for in situ lunar surveys will be subject to a random scattering of sensors, resulting in random network connectivity and coverage. Models reflecting this property, as well as the three-dimensional terrain, lifetime, and mass constraints of the mission, are introduced. From them, an optimization problem solved with simulated annealing is defined, enabling selection of the best deployment configuration in order to characterize water distribution in the scenario recently described by Klima et al. in the Bullialdus Crater on the Moon.

The robotic task sequencing problem (RTSP) appears in various forms across many industrial applications and consists of developing an optimal sequence of motions to visit a set of target points defined in a task space. Developing solutions to problems involving complex spatial constraints remains challenging due to the existence of multiple inverse kinematic solutions and the requirements for collision avoidance. So far existing studies have been limited to relaxed RTSPs involving a small number of target points and relatively uncluttered environments. When extending existing methods to problems involving greater spatial constraints and large sets of target points, they either require substantially long planning times or are unable to obtain high-quality solutions. To this end, this article presents a clustering-based algorithm to efficiently address spatially constrained RTSPs involving several hundred to thousands of points. Through a series of benchmarks, we show that the proposed algorithm outperforms the state-of-the-art in terms of solution quality and planning efficiency for large, complex problems, achieving up to 60% reduction in task execution time and 91% reduction in computation time.

This paper proposes a novel optimal planning model for combined heat and power (CHP) in multiple energy systems of natural gas and electricity to benefit both networks by deferring investment for network owners and reducing use-of-system (UoS) charge for network users. The new planning model considers the technical constraints of both electricity and natural gas systems. A two-stage planning approach is proposed to determine the optimal site and size of CHPs. In the first stage, a long-run incremental cost matrix is designed to reflect CHP locational impact on both natural gas and electricity network investment, used as a criterion to choose the optimal location. In the second stage, CHP size is determined by solving an integrated optimal model with the objective to minimize total incremental network investment costs. The proposed method is resolved by the interior-point method and implemented on a practically integrated electricity and natural gas systems. Two case studies are conducted to test the performance for single and multiple CHPs cases. This paper enables cost-efficient CHP planning to benefit integrated natural gas and electricity networks and network users in terms of reduced network investment cost and consequently reduced UoS charges.

While Soft Open Point (SOP) shows its strong capability to control load transfer and regulate network voltage profile in a distribution network, there are limited considerations of the high investment costs of these special electric devices. This gap may hinder optimal planning of an SOP and even affect its wide usage in a distribution network. This paper proposes an optimal planning method for an SOP that fully considers the cost of an SOP in a coastal network where typhoon frequently lands. The benefit of the SOP consists of the reduction of energy loss in daily operations and the post-fault supply restorations under extreme typhoon environment. The evaluation of the benefit of the SOP from the reduction of energy loss in daily operations is based on simulations of multiple scenarios in the long-term life cycle of the SOP. In order to evaluate the damages to the distribution network from typhoon, the virtual typhoon samples are generated from historical data in the entire period of the life cycle of the SOP. A practical algorithm is designed for the scanning of the possible formations of islands that can be restored by an SOP when typhoon lands. In the end of the paper, both the location and the capacity of an SOP are optimized considering the balance among the benefit of energy-loss reduction in daily operations, the benefit of supply restoration after typhoon lands, and the investment and operation costs of the SOP. From numerous simulations, it is validated that implementing only one SOP in the distribution network is both optimal and practical in most simulation cases due to the restrictions of the extremely high implement costs of SOP, and this may greatly simplify the complexity of the optimal planning problem for the SOP in a distribution network.

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.

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.

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.

In this paper, a dynamic planning approach is presented in order to determine the optimal number, location, capacity and time for construction or development of electric vehicles (EVs) parking lots in a distribution system. Estimating the power exchange between the EVs and parking lots is a fundamental prerequisite in parking lot planning. The amount of power exchanged depends on how EVs are charged and discharged. Hence, an innovative method is presented in order to scheduling of EV charge and discharge as a subsection of parking lot dynamic planning. Also, to select the most effective parking lot, a criterion based on the energy required to reach the parking lot is proposed which is able to minimize both the time and energy required to arrive at the parking lot. Moreover, the uncertainty in the investment required and the budget allocated to construction and development of the parking lots are modeled. In order to maximize the parking lot operator profit, revenues and costs in the parking lot lifecycle are formulated. By investigating some problems including budget shortage, its uncertainty and allocation type in different years; effects of the budget on dynamic planning of EV parking lots are studied. Also, the influence of the growth rate of EVs is also addressed by defining different scenarios. The results show that the proposed approach is able to perform the planning of EV parking lot in an optimal way leading to the maximum possible profit while considering financial limitations and system constraints. Also, adopting a proper approach to allocate the budget in different years is completely dependent on the way EVs grow in the network. Moreover, an increase in the budget uncertainty affects both the parking lot planning and gains of investment.

In this paper, we propose a new scheme for designing multi-carrier networked microgrids. Various multi-energy carriers are utilized to form energy hubs which exchange energy with each other and the main electricity and gas networks. The proposed model considers renewable generation, diesel generators, fuel cells, energy storages, combined heat and powers, boilers, combined cooling heat and powers, and power to gas converters. The seasonal load patterns are considered to design the optimal capacity of combined cooling, heating, and powers. The main objective of the proposed model is to operate the integrated hubs with minimum cost, meet the consumer’s required energies, reduce the emissions, and create a bi-directional interaction between electricity, and gas networks using power to gas converters. While all electrical, mechanical, and technical constraints are satisfied. To achieve these improvements, industrial and residential hubs interact with other multi-carrier microgrids to determine the optimal operation of hubs. The proposed model is tested on a standard case study and results show when hubs integrate about the total daily cost of each microgrid has been improved about 200 $. Besides, the thermal energy not supply in microgrid 4 has been improved from 4.61 MWh to 0.45 MWh.

The combined steepest decent and artificial annealing optimization method for optimal planning of radial distribution networks is upgraded to take into account the uncertainty of inputs and various methods of determining the load curtailment costs. The optimization method considers the uncertainties of predicted power consumption, of line failure rates and of the power supplied by distributed generators. The search for the optimal network configuration is conducted with various cost models for the curtailed loads, for comparison. The calculations performed have shown that the reliability cost models considerably affect the optimal network configurations and associated total annual costs. Also, it is indicated that the distributed generators should be modeled in the planning of networks as they favorably affect the total costs by decreasing energy losses and by helping in maintaining network operation within the allowed current and voltage limits.

The growing penetration of wind power in a power system brings great challenges to system operation flexibility. For generation planning in presence of high wind power penetration, it is essential to take the operation flexibility of the system into account. Firstly, this paper developed the system operation flexibility metrics through considering the flexibility contribution of thermal generating units (TGUs) by operational state transition. Secondly, a planning model for the bundled wind-thermal-storage generation system (BWTSGS) that considers the operation flexibility constraints is proposed. The planning model is used to determine the power and energy rating of an energy storage system (ESS) as well as the type and number of TGUs. A daily scheduling simulation model of a BWTSGS is proposed to calculate the operation cost for the planning model and consider the sequential operation characteristics of the BWTSGS. Further, in order to accelerate the computation, a wind power sequential clustering technique based on the discrete Fourier transform (DFT) method is developed for improving the computational efficiency. Case studies have been conducted on a 1000-MW wind farm to demonstrate the validity and effectiveness of the proposed model.

Existing multi-screening technologies have been limited to mirroring the current screen of the smartphone onto all the connected external display devices. In contrast, NANS (N-App N-Screen) technology is able to display different applications (N-App) on different multiple display devices (N-Screen) using only a smartphone. For such NANS services, this paper empirically shows that the thermal violation constraint is more critical than the battery life constraint. For preventing the thermal violation, the existing DTM (Dynamic Thermal Management) techniques cannot be used since they consider thermal violations as abnormal, and hence prevent them by severely throttling CPU frequencies resulting in serious QoS degradation. In NANS service scenarios it is normal to operate in high temperature ranges to continue services with acceptable QoS. Targeting such scenarios, we first propose a novel thermal prediction method specially designed for NANS services. Based on the novel thermal prediction method, we then propose a novel DTM technique called, &ldquo, thermal planning&rdquo, to provide sustainable NANS services with sufficiently high QoS without thermal violations.

It is anticipated that the penetration of “Green-Energy” vehicles, including Electric Vehicle (EV), Fuel Cell Vehicle (FCV), and Natural Gas Vehicle (NGV) will keep increasing in next decades. The demand of refueling stations will correspondingly increase for refueling these “Green-Energy” vehicles. While such kinds of “Green-Energy” vehicles can provide both social and economic benefits, effective management of refueling various kinds of these vehicles is necessary to maintain vehicle users’ comfortabilities and refueling station’s return on investment. To tackle these problems, this paper proposes a novel energy management approach for hybrid refueling stations with EV chargers, Hydrogen pumps and gas pumps. Firstly, the detailed models of EV chargers, Hydrogen pumps with electrolyte and hydrogen tank, the gas pumps with gas tank, renewable resources, and battery energy storage systems are established. The forecasting methodologies for renewable energy, electricity price and the traffic flow are also presented to support the hybrid refueling station modeling and operation. Then, a management approach is adopted to manage the refueling various kinds of vehicles with considerations of the refueling station profitability. Finally, the proposed management approach is verified under four different kinds of tariffs- Economy-7, Economy-10, Flat-rate, and Real-Time Pricing (RTP), finding that the proposed management approach has the best performance under RTP tariff. The economic assessment of the Energy Storage System (ESS) is also performed. It is found that the ESS can make the saving up to $127 per day. Different sizes of gas storage tank are compared in the final section as well. The result shows that increasing the size of the tank does not bring attractive extra benefits with the consideration of the investment on enlarging the tank size.