Search

Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system. [ABSTRACT FROM AUTHOR]

This study presents a mathematical model to determine the optimal site and the size of plug‐in hybrid electric vehicles charging stations (CSs) in the distribution networks. The presented optimal planning is done along with considering the rate of customers' participation in demand response programs (DRPs) at the presence of some uncertainties associated with the load values and electricity market price. In order to model the load, time‐of‐use DRP is used. The proposed model involves distribution system manager (DSM) benefit maximising derived from the appropriate use of CS for charging and discharging vehicle batteries, reliability improvement and supplying network's load demand at peak times as the objective function, technical limitation as constraint and the siting and sizing of electric vehicles CS as optimisation variables. The genetic algorithm method with embedded Monte Carlo simulation is used in order to solve the optimisation problem and is applied to the 9‐bus and 33‐bus networks. The test results indicate that not only does the appropriate planning have an economic benefit for DSM, but also voltage profile and power supply reliability for customers can be improved and active power losses can also be reduced. [ABSTRACT FROM AUTHOR]

This study proposes long‐term dynamic planning of distributed generation (DG) units in distribution networks considering active management (AM). In the AM operation mode, the distribution network equipment is controlled in real time based on the real‐time measurements of system parameters (voltage and current). The proposed model determines the optimal size, location and time of investment of DG units. The uncertainty of energy price and rate of load growth is handled using a scenario‐based modelling approach. To demonstrate the advantages of dynamic DG planning, its results are compared with those of static model. Also the results of optimal DG planning in active network are compared with those of the passive operation mode. In addition, a technical evaluation and comparison between conventional, passive and active networks have been performed. The proposed model is successfully applied to the 33‐bus radial distribution network. The results indicate that using AM, the cost function and losses are decreased effectively compared to the passive management. Computation of the optimal DG planning in AM environment is formulated as a mixed integer non‐linear problem which can be solved using commercial optimisation packages like as GAMS. [ABSTRACT FROM AUTHOR]

In the near future, millions of electric vehicles (EVs), especially plug‐in hybrid EVs (PHEVs), will be integrated into the electrical system. Such an increase in the demand for energy will require optimised management of distribution networks. This study presents a new formulation to determine the optimal size and location of PHEV charging stations to maximise the distribution system manager (DSM) benefit. A two‐stage procedure has been considered to optimise PHEV charging station planning. In the first stage, the candidate locations and their size are determined based on: (i) the sum of benefits from the appropriate use of charging station for charging and discharging of vehicles and (ii) the benefit of providing spinning reserve and supplying the network's load demand at peak times by DSM. In the next stage, an improved framework is proposed to classify charging stations into efficient and inefficient sets characterised by non‐financial indices. The proposed method is data envelopment analysis efficiency and three indices, including active power loss, voltage profile improvement and CO2 emission. The suggested model is successfully applied to a test system with nine buses. Simulation results show that appropriate planning results in economic benefits as well as improvements in active power losses and the voltage profile. [ABSTRACT FROM AUTHOR]

In this paper, we applied the Data Envelopment Analysis (DEA) ranking method to have efficient placement of Distributed Generation (DG) in distribution network. In this regard first an analytical method to find the optimal size of DG in the network is used to reach the lowest possible losses. In this paper, benchmarks such as improvement of voltage profile, reducing energy not supplied value (as an index of reliability), reducing environmental pollution, and values related to the purchase and installation costs of DG equipment in each busses for selecting the appropriate DG location are considered, in addition to the network loss reduction. This method has been used because the loss reduction of whole the network will not be a complete criteria for selecting the best location to install DG, The necessarily node which has the highest reduction in power losses cannot be considered suitable node for the installation of DG. Therefore we used DEA to determine the most effective location for DG placement. The proposed method is implementation over the network of 33 buses and the results are presented. GAMS software is used for the simulation results extraction. [ABSTRACT FROM AUTHOR]

If determination of location and size of Distributed Generation (DG) are applied accurately, the DG's ability will improve the network situation and reduce operation costs. In this paper, various market conditions are considered to maximize the benefit of DG's presence and make a trade off among advantages of DG, network situation, and Distribution Company (DISCO) owners. To determine the optimal location and size of DG, two methods of the cost minimization and the nodal pricing are combined. In addition to evaluating the impact of parameters such as variation of energy price and load on objective function, effect of these parameters on location and size of DG is considered. To confirm the results, impact of loads which are dependent on voltage and variation of the power factor of the DG units is applied and then effect of power factor on optimal location and size of DG is shown. A method is proposed for convergence of different results which is caused by different power factors. To observe long-term impact of the DG's presence in the network, a load growth for five years is considered annually. Study is carried out on IEEE30 bus test circuit. [ABSTRACT FROM AUTHOR]

One of the main purposes of distribution network operators is increasing the participation of distributed generations (DGs). The DGs contribute in reduction of power losses, improvement of voltage profile and reliability and the postponement of network reinforcement costs. New control methods are required for DG management in order to provide more flexibility in distribution networks. In this study, the authors discuss a specific aspect of active management (AM), which is an effective approach for increasing the penetration of DG units and their management. AM is based on using the control and communication links for better operation of DGs. The effect of AM on the DG integrated distribution network in an uncertain environment is assessed. Fuzzy technique is implemented for modelling the uncertainty of loads, generation of DGs, purchased power from the grid and electricity price. By considering an uncertain environment, in this study, the effect of AM on decreasing the risk of technical issues (active loss and voltage profile) in distribution network is evaluated. [ABSTRACT FROM AUTHOR]

One of the ways to manage and provide flexibility in power systems is demand response (DR). A large number of end-users as DR sources must be aggregated by an intermediate entity called DR aggregator. This paper proposes an approach based on game theory to obtain the best bidding strategy of DR aggregators in electricity market. In the presented scheme, an economic responsive load model is employed for DR approach which is based on customer benefit function and price elasticity. In this paper, the network operator receives DR services from the DR aggregator. It is considered that all bids from aggregators are assembled by a network operator which calculates the share of each aggregator in DR programs by revenue function optimization. Furthermore, the network operator offers rewards to DR aggregators to achieve this purpose. The robust optimization (RO) method is used handling price uncertainty. It is used to optimize the robustness of the decision-making strategies. A non-cooperative game is used to model the competition among DR aggregators. The Nash equilibrium idea is employed to solve this game. • Obtaining optimal robust bidding strategy of DR aggregators in the day-ahead market. • A non-cooperative static game with incomplete information is used to model competition. • A responsive load economic model is applied to DR implementation. • DR program is based on price elasticity and customer benefit function. • The network operator receives bids from aggregators and specifies the share of each aggregator. [ABSTRACT FROM AUTHOR]

This paper presents two centralized adaptive under frequency load shedding (UFLS) methods based on loads willing to pay (WTP) and new objective function. The objective function is considered to minimize load shedding penalties that are paid by distribution network operator (DNO), using optimal algorithm. Maintaining DG units in an islanded distribution system is proposed to prevent system blackout. One of the most important protection schemes to prevent a system blackout is UFLS method. In our proposed UFLS method, amount of load shedding is dependent on loads priority table and intensity of event. Loads priority table is formed, using rate of change of frequency of loads (ROCOFL) indices. Proposed techniques apply the amount of loads active power every few minutes to update loads priority table. These proposed UFLS methods are implemented on the 14-bus Danish distribution system in 3 different load scenarios and are compared with a conventional approach. [ABSTRACT FROM AUTHOR]