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Increasing penetration of distributed renewable energy sources (DRESs) has resulted in the emergence of distributed energy resource aggregators (DERAs). A DERA participates in the transmission-level market operated by a transmission system operator (TSO), and the DERA’s resources are connected to a jurisdiction of the distribution system operator (DSO). Inspired by the structure of the Korean power industry, this study assumes a minimal DSO that cannot directly dispatch the resources in its system. In this study, we develop a detailed procedure for prequalification wherein the DSO checks the DERA’s bids that are submitted to the TSO markets. The proposed prequalification enables the DSO to secure the reliability of its system by providing limited network information to the DERA. The DERA modifies its bid until potential overvoltage and overflow problems are resolved, even in the worst case, including uncertainties. The proposed prequalification process is verified using the IEEE 33-bus distribution network. Compared to previous studies, the results demonstrate that the proposed prequalification can deal with distribution system constraints, even though uncertainties are included. The proposed prequalification process can be applied to power industries where the DSO does not have the full dispatch authority on DRESs.

The concept of a virtual power plant (VPP) was introduced for supporting market participation of small-scale renewable energy-based generators (REGs). In a VPP, energy storage systems (ESSs) and micro-turbine-based generators (MTGs) are used together to mitigate the variability of the output of REGs. To keep the output of a VPP at the contracted value in a market, centralized feeder flow control (FFC) methods can be adopted for the VPP. However, conventional centralized FFC methods lack careful consideration of different characteristics of various resources and require high-speed communications and several central control units. Therefore, this paper proposes a new FFC method considering various characteristics of resources and enabling operation with less communication dependency. The effectiveness of the proposed method is verified by comparing the results of the proposed method with those of the conventional method with performance analysis, stability analysis, and simulations with Simulink/MATLAB.

This paper presents a peer-to-peer (P2P) energy auction mechanism based on transaction zoning in a distribution system with a number of distributed generators. A transaction zone can be configured by the power flow monitoring method using the bids and offers from auction participants. In this study, the power flow monitoring method was applied to check how much power flow passes through branches and divide the whole network into sub-networks based on branched with small power flows. Energy trading in the transaction zone configured in this manner has no significant effect on other zones in terms of power flow because only little power flow passes through the branch, which is connected in adjacent to the transaction zone. Therefore, supply and demand can be balanced in the transaction zone and energy trading can be established without violating network constraints. To verify the effectiveness of the transaction zoning, the proposed P2P trading environment was simulated with an IEEE 37-node test feeder. Simulation result shows that P2P energy transaction using transaction zone does not violate network constraints.

The Korean government announced a new energy transition policy that emphasizes phasing out nuclear and coal energy and increasing renewable energy sources. However, a lack of quantitative research has resulted in disagreements about the most suitable energy transition strategy for Korea. To evaluate the policy, we designed a quantitative analysis that simulates generation scheduling and settlement processes in the Korean power market. We then analyze the economic impacts, conflicts of interest, greenhouse gas (GHG) emissions, and the power system’s market price sensitivity to the price of imported liquefied natural gas (LNG) in four energy transition scenarios: i) the government’s new energy transition policy, ii) phase-out of nuclear energy, iii) phase-out of coal energy, and iv) simultaneous phase-out of nuclear and coal energy. Additionally, we evaluated the effects of coal taxation. Based on the power market simulation results, we conclude that phasing out nuclear energy with increasing taxation of coal, or only phasing out coal energy are two reasonable scenarios for energy transition in Korea. The simultaneous phase-out of nuclear and coal energy, which was originally pursued by the government, is inferior to the recommended scenarios in the aspects evaluated. Policymakers should consider these results when developing feasible energy transition scenarios.

Although the Korean government plans to increase its share of variable renewable energies (VREs), the Korean power market is not sufficiently mature to accommodate a large increase in VRE generation. Thus, the Korean system operator plans to introduce a two-settlement, and an imbalance settlement is also under consideration, among several options. Therefore, this study analyzes how many incentives are given for prediction accuracy under several imbalance settlement schemes adopted from European and US power markets. Results show that the imbalance settlement consisting of threshold and penalty terms is useful for rule-makers, who can control revenue differences between the groups with different prediction accuracies by adjusting the two terms. The suggestion given in the paper will be useful for not only the Korean power market but also for the countries that plan to establish the imbalance settlement rules while increasing renewable energy.

Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.

The conventional microgrid (MG) price-based operation scheme with respect to the hourly market price considers only profit maximization from energy transactions and disregards variability. This causes flexibility burdens on the main grid system operator (SO), which must then utilize its ramping capability to cover the net load variability. As the proportion of renewable energy sources (RESs) involving intermittency in MGs continues to increase owing to global energy policies, net load variability within shorter time intervals has also increased, making proper management guidelines necessary. Thus, this paper proposes an MG-SO variability contract on intra-hour and inter-hour time intervals for regulating variability such that the SO can support and distribute its relevant costs between the MG and the SO. To prove the effectiveness of the proposed contract, an MG variability contract-based scheduling model is also proposed, and the results were compared with those of the price-based model. A case study demonstrates that the introduction of RESs increases the variability in shorter intervals and that the suggested contract is effective in terms of decreasing the variability with increased MG operating costs. A sensitivity analysis between the reduced variability and additional operating costs was also conducted in the case study.

This paper proposes Load Serving Entities (LSEs) model that purchases energy in the wholesale market by optimally operating Critical Peak Pricing (CPP) considering Electric Vehicle (EV) load and photovoltaic (PV) generation. In the proposed scheme, LSEs determine critical event schedule and hourly bidding schedule of band capacity in power exchange for frequency control (PXFC) market. In addition, imbalance penalties due to various uncertain parameters are involved in the stochastic optimization problem with Monte Carlo scenarios. This problem is formulated as mixed integer nonlinear program (MINLP). In the simulation section, the optimal critical event schedule described in this paper is numerically verified. Also we show the profit of LSEs is affected by level of uncertain parameters. This result provides intuition that several factors should be considered in the critical peak event operating strategy of LSEs not only market conditions but also characteristics of demands and uncertainty parameters.

The sizable electrical load of plug-in electric vehicles may cause a severe low-voltage problem in a distribution network. The voltage drop in a distribution network can be mitigated by limiting the power consumption of a charging station. Then, the charging station operator needs a method for appropriately distributing the restricted power to all plug-in electric vehicles. The existing approaches have practical limitation in terms of the availability of future information and the execution time. Therefore, this study suggests a heuristic method based on priority indexes for fairly distributing the constrained power to all plug-in electric vehicles. In the proposed method, PEVs are ranked using the priority index, which is determined in real time, such that a near-optimal solution can be obtained within a short computation time. Simulations demonstrate that the proposed method is effective in implementation, although its performance is slightly worse than that of the optimal case.

There are several types of load in distribution system. The load value that represents the varying load with time is needed to be estimated for reconfiguration because the system is reconfigured in non-real time. Specially, as the power system develops as the smart grid, the amount of distributed generations is increased. Since the variability of distributed generations’ output is higher compared to normal generations, it is important that which representative value is used for variable output when an algorithm performs. In addition, the reversed current caused by distributed generation should be considered. In this paper, we propose a method to choose representative value considering distribution generation and variable load and also a constraint considering the reversed current. By this method, we study the distribution system reconfiguration for reliable and efficient operating.

With increasing number of distributed renewable energy sources integrated in power distribution networks, network security issues such as line overloading or bus voltage violations are becoming increasingly common. Traditional capital-intensive system reinforcements could lead to overinvestment. Moreover, active network management solutions, which have emerged as important alternatives, may become a financial burden for distribution system operators or reduce profits for owners of distributed renewable energy sources, or both. To address these limitations, this paper proposes an online network reconfiguration scheme based on a deep reinforcement learning approach. In this scheme, the distribution network operator modifies the network topology to change the power flow when the reliability of network is threatened. Because the variability of distributed renewable energy is large in self-sufficient distribution networks, the reconfiguration process needs to be performed online within short time intervals, which involves the use of conventional algorithms. To solve this problem efficiently, a deep q-learning model is utilized to determine the optimal network topology. Performances of proposed and other algorithms were compared in modified CIGRE 14-bus and IEEE 123-bus test network, as well as varying penalties for frequent switching operation in consideration of physical characteristic of the network. Simulation results demonstrated that the proposed algorithm showed almost identical performances with brute-force search algorithm in both test networks, satisfying network constraints over almost all timespans. Further, the proposed method required very small computation times - under a second per each state and its scalability was verified by comparing the computation time between two test networks.

A Micro Grid is an aggregate of many small-scale distributed energy resources (DERs), loads and can be operated independently or together with the existing power grid as a local power grid. The operator of such a grid takes charge of the energy supply and consumption of these resources and loads available in the grid. Meanwhile, the system operator of the grid considers the entire Micro Grid system to be a single load or a generator and assigns the responsibility of its internal management to the operator. The power production from a passive production resource is largely influenced by external environmental factors such as weather conditions, rather than operating conditions. Thus, this study conducted simulations for the cases where four kinds of conditional expressions had not been applied at all or one of them had been applied to compare and evaluate the effectiveness of each expression. As a result, the conditional equations were found to be effective when attempting to optimize the Micro Grids efficiently.

A thermostatically controlled load (TCL) can be one of the most appropriate resources for demand response (DR) in a smart grid environment. DR capability can be effectively implemented in a TCL with various intelligent control methods. However, because traditional on-off control is still a commonly used method in a TCL, it is useful to develop a method for adding DR capability to the TCL with an existing on-off controller. As a specific realization of supervisory control for implementing DR capability in the TCL, two methods are proposed - a method involving the changing of a set point and a method involving the paralleling of an identified system without delay. The proposed methods are analyzed through the simulations with an electric heater for different power consumption levels in the on-state. Considerable cost benefit can be achieved with the proposed methods when compared with the case without DR. In addition, the observations suggest that a medium power consumption level, instead of the maximum power, in the on-state should be used for consistently obtaining the cost benefit without severe temperature deviation from the specified temperature range for DR.

The utilization of electric vehicles has been suggested to support the frequency regulation of power system. Assuming that an intermediate aggregator exists, this study suggests a decentralized vehicle-to-grid operation scheme in which each vehicle-to-grid aggregator can behave independently of the power system operator. To implement this type of decentralized operation, this study adopts a price-based operation that has been proposed by many researches as an alternative operation scheme for the power system. In this environment, each vehicle-to-grid aggregator can determine its participation in vehicle-to-grid service in consideration of its residual energy of aggregated system and real-time market price. Consequently, the main purpose of this study is to verify whether or not the vehicle-to-grid power can effectively support the current frequency regulation function within the price-based operation scheme. Specifically, a frequency regulation method is proposed based on the real-time price signal, and a feedback controller for battery management is designed for decentralized vehicle-to-grid operation.

This paper presents the important features of structure-dependent model predictive control (MPC)-based approaches for automatic generation control (AGC) considering network topology. Since power systems have various generators under different topologies, it is necessary to reflect the characteristics of generators in power networks and the control system structures in order to improve the dynamic performance of AGC. Specifically, considering control system structures is very important because not only can the topological problems be reduced, but also a computing system for AGC in a bulk-power system can be realized. Based on these considerations, we propose new schemes in the proposed controller for minimizing inadvertent line flows and computational burden, which strengthen the advantages of MPC-based approach for AGC. Analysis and simulation results in the IEEE 39-bus model system show different dynamic behaviors among structure-dependent control schemes and possible improvements in computational burden via the proposed control scheme while system operators in each balancing area consider physical load reference ramp constraints among generators.

A high-speed earthing switch (HSES) of a gas-insulated switchgear (GIS) performs a secondary role in protecting the power system in the event of an accident. After being interrupted by a circuit breaker (CB), the short-circuit current remaining in the network should be earthed by the HSES. However, as the contacts of the HSES may have been damaged by a pre-arc during the closing operation, the HSES may fail to ground the short-circuit current. To successfully ground the short-circuit current, the contacts of the HSES must be protected from damage by the pre-arc. Thus, minimizing the pre-arcing time is required to step up the making performance. This paper analyzes the making performance of the HSES by predicting the pre-arcing time within the short-circuit current making test. To estimate the pre-arcing time, we compared the results of analytical calculations based on streamer discharge theory and the numerical simulations of the electric field strength. By conducting a short-circuit current making test for a 420 kV 63 kA HSES in a high-power laboratory, the pre-arcing time calculation results were verified. A comparison of the results showed that the proposed prediction method was useful for verifying the performance of the HSES.

Future power systems are expected to have distributed energy resources (DERs). A price-based operation (PBO), where dynamic prices are used as the control signal, can be an alternative scheme to address challenging operational issues in the future power systems. In this paper, a decentralized framework for optimal PBO using a feedback control mechanism is proposed to determine the nodal prices for power balance and congestion management. The substructures and feedback controllers of the proposed framework are derived based on the optimal power flow (OPF) method. Thus, the framework guarantees optimality for all situations in real-time and enables the use of various types of controllers. The effectiveness of the proposed framework is verified with the IEEE 39 bus network under some scenarios, such as the failure of a generator and a transmission line. The results clearly demonstrate that the proposed framework successfully resolves the balance and congestion problems by generating appropriate nodal prices in the PBO and provides a solution similar to the optimal solution determined by the conventional OPF method.

pellet electrode wereinvestigated in aqueous electrolyte solutions. The flat-band potential of this electrode, determined using a Mott-Schottky plot, was +0.18 V vs SCE at pH 4.8 and followed the Nernst equation with respect to pH. Under UVlight illumination, a cathodic photocurrent developed, and molecular hydrogen simultaneously evolved on thesurface of the electrode due to the direct reduction of water without deposition of any metal catalyst. Key Words : CuGaO

This article investigates and compares various types of microgrids in the literature. The comparison is concerned with the economic operation of the micro-sources employed in different topologies and the reliability of the end-users. Also, the effect of each component on the performance of the overall microgrid is discussed in detail, e.g., the individual and centralized power converters, etc. The numerical simulation of a case study shows that the single AC and three wire network microgrid types are predominant over two others, namely single DC and hybrid AC-DC networks. The AC microgrid shows its benefits in market operations, while the three-wire network takes advantage of customizing loads as its reliability preferences. The DC microgrid is the worst case, suffering from high power losses and poor reliability.

This paper addresses the scheduling and operation problem of a combined battery and wind generation system (BWGS) in real-time market. The proposed operation scheme consists of two control mechanisms: (i) offline scheduling according to the estimation of the market prices and the stochastic information of wind generation, and (ii) online dispatching based on the updated system state in real time. The analysis in this paper is different from the previous studies by taking into account the expense of battery energy storage under a newly defined term: cost of lifetime losses (CLLs). Thus the optimal scheduling and operation of BWGS not only gives maximum profit to the owner but also avoids the abusive operation of the battery. The case study provides a comparison of the proposed operation scheme with other approaches. It is shown that the proposed scheme considering the CLLs can significantly change the optimal charging/discharging of battery energy storage, and the increase in profit obtained in this case is about 9%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The capacity of battery energy storage systems (BESS) is expected to increase for power system applications. However, as the cost of BESS is high, economic feasibility must be considered when using BESS in grid applications. Load leveling with BESS is one such application for which the economic implications have been analyzed in the literature. However, these studies do not sufficiently consider the fact that the leveled loads will lead to a change in electricity prices, thereby modifying charging/discharging operations of BESS. Additionally, in a competitive electricity market, electricity prices are not determined by the generator cost functions. Market participants’ strategic decisions also affect prices. Therefore, we conducted an economic analysis of load leveling with BESS in an electricity market from the perspective of a utility company and/or a government agency. In our analysis of the Korean market, we examine whether the leveled loads necessarily lead to economic benefits. Load leveling performance and the associated economic benefit are quantitatively analyzed for varying sizes of BESS. Further, the policy implications related to using BESS are derived from the analysis results.

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an objective function representing the information and costs pertaining to electricity use, while minimizing its value by using Mixed Integer Linear Programming or a Genetic Algorithm. The main feature of the software is that an optimal operation schedule derivation function has been implemented with MATLAB for the following circumstances: when the basic operation rules are applied, when operating with another grid, when the external operating conditions are applied, and when the internal operating conditions are applied.

In a deregulated power system, the independent system operator (ISO) becomes the central entity which is responsible for the reliable system operation and the real-time balancing. ISO can provide necessary functions through the market mechanism. However, the possibility of a price-based operation is suggested as well, in which time-varying price coordinates non-cooperative players' actions. In the price-based operation, the main objectives of operation are also to keep the system frequency to the nominal value and to manage the line congestion. In terms of the frequency, the price-based operation should at least assist the frequency regulation functions by reducing the frequency deviation. For this purpose, this paper tries to present a method to reduce the frequency deviation and proposes the application of PID tuning rule to the design of price-based operation. In addition, a modified asymmetric control structure is proposed to obtain a further reduction of the frequency deviation. The performance and suitability of the designing rule are verified through simulations using the IEEE 39-bus network.

Wind power producers face many regulation costs in deregulated environment, which remarkably lowers the value of wind power in comparison with conventional sources. One of these costs is associated with the real-time variation of power output and being paid in frequency control market according to the variation band. This paper presents a new approach to coordination of battery energy storage in wind generation system for reducing the payment in frequency control market. The approach depends on the statistic data of wind generation and the prediction of frequency control market price to determine the optimal variation band which is then kept by the real-time charging and discharging of batteries, ultimately the minimum cost of frequency regulation can be obtained. The optimization problem is formulated as trade-off between the decrease in the regulation payment and the increase in the cost of using battery, and vice versus. The approach is applied to a study case and the results of simulation show its effectiveness.

Wind power producers face many regulation costs in deregulated environment, which remarkably lowers the value of wind power in comparison with the conventional sources. One of these costs is associated with the real-time variation of power output and being paid in frequency control market according to the variation band. In this regard, this paper presents a new approach to the scheduling and operation of battery energy storage installed in wind generation system. This approach depends on the statistic data of wind generation and the prediction of frequency control market prices to determine the optimal charging and discharging of batteries in real-time, which ultimately gives the minimum cost of frequency regulation for wind power producers. The optimization problem is formulated as the trade-off between the decrease in regulation payment and the increase in the cost of using battery energy storage. The approach is illustrated in the case study and the results of simulation show its effectiveness.

Recently, Korean system operating conditions have gradually approached an upper limit. When a contingency occurs, the power system may have no solutions. Different from the cases of bad initial guesses or the solutions are too close to the solvability boundary in which numerical methods can be applied, for unsolvable cases, the only way to restore solvability would be structure modifications. In this paper, a new approach for corrective and preventive control to such cases is proposed in two steps: (i) finding any solution regardless its feasibility; (ii) for the infeasible solution, make it feasible with additional modifications at load buses having Distributed Energy Resources. The test case built based on the peak load profile of 2008 by KEPCO including 1336 buses is analyzed. Since reactive power compensation is optimized to restore solvability, all demands are met, therefore no blackouts happen. The proposed method was integrated in the LP program designed by power21 Corporation.

– This paper presents the temperature impacts on daily peak load of commercial load in KOREA. This research focuses on the overall summer and winter peak load characteristics and the impact of temperature change on the commercial load. Load data of the last five years are used to analyze the temperature sensitivity of commercial load sensitivity. Keywords : Temperature sensitivity, Commercial load, Peak load, Regression analysis 1. Introduction In the market environment it is becoming increasingly more important to obtain a more accurate model of the usage pattern of electricity consumers for establishing related policies. For example, the amount of generation investment to be made, the appropriate tariff for electricity, the proper level of demand-side management (DSM), etc. are all heavily dependent on the load pattern. In the past, only limited representative consumer load data were available due to the insufficient infrastructure for taking such measurement [2]. However, with the Automatic Meter Reading (AMR) widely deployed it became relatively easier to gather data about many consumers. The electricity load characteristics are being considered in many areas: for example, in performing demand forecast [2,3,8], in computing price elasticity [5], and in designing of DSM and electricity tariffs [6], and in managing various assets [8]. In [4] the temperature sensitivity of load is studied for sample consumers who are selected considering the population of commercial, industrial and residential consumers in Taiwan. In [7] the temperature sensitivity of industry representative load pattern in United States is derived. The temperature sensitivity of electricity load is considered critical with the expansion of electricity-based cooling and heating equipment. In this paper the temperature sensitivity of the commercial load is derived using the Korea’s recent winter and summer power consumptions. In Section 2 the percentage of the sample data is compared. In section 3 summer/winter load pattern and composition are described. Section 4 presents the results of investigating winter and summer temperature sensitivity.

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Island operation of a microgrid increases operation survivability and reliability when there is a large accident in a main grid. However, because a microgrid typically has limited generation capability, a microgrid operator (MGO) has to take the risk of island operation into account in its market participation and generation scheduling to ensure efficient operation. In this paper, a microgrid islanding event is interpreted as a trade suspension of a contract, and a set of islanding rules is presented in the form of a market rule. The risk of island operation is evaluated by modeling the microgrid islanding stochastically using an islanding probability function, which is defined in the form of a conditional probability to reflect the influence of outside conditions. An optimal bidding strategy is obtained for the MGO by formulating and solving an optimization problem to minimize the expected operating cost. The effectiveness of the proposed method was investigated by numerical simulations in which the proposed method and two other methods were applied to the same microgrid. Numerical sensitivity analyses of the coefficients of the islanding probability function were conducted to determine how an MGO copes with changes in outside conditions.

For developing a large-scale combined system with a number of distributed resources, an appropriate compensation strategy based on the system components and changeable condition must be configured to handle the characteristics of the internal systems. Since renewable sources generate various fluctuations, the compensation plans for the storage device connected along with the sources should be supported by a precise expectation method. A cooperative strategy involving the sharing of the DC section with environmentally sensitive generators, like photovoltaic system (PVs) or waves, demands appropriate ESS compensation solutions, owing to its complexity. An active power-control algorithm with voltage-expectation based on the DC power flow is introduced in this paper and is applied in the designed case studies performed on the electromagnetic transient simulation. DC based multi-generation system is composed by applying tidal generator and super capacitor. To utilize wind energy, an offshore wind–wave generation system was utilized in the verification process.

The optimal planning and operation of integrated energy systems have more economic and environmental advantages than individually optimized systems because of the increasing interdependency between different energy systems. A combined heat and power generation system (CHP) generates electricity and heat simultaneously and is expected to play a key role in both power systems and heat supply systems. An average variable cost (AVC) has been used in planning problems to determine the optimal capacity of generators, boilers, and CHPs. The AVC is calculated from historical data without considering operating points which are affected by demand characteristics. In addition, in the process of calculating AVCs, the heat output of CHPs is neglected and only the power output is considered. In this paper, an AVC calculation method is proposed for the planning problem of a cogeneration system. We suggest a two-dimensional linearized variable cost model to calculate the AVCs simpler and formulate a scheduling problem including the suggested model as mixed integer linear programming (MILP). The problem is solved by general algebraic modeling system (GAMS) and the results are used for calculating AVCs not only in power but also in heat part. Demand patterns in Korea are used to find the operation points of CHP and AVCs.

To address the environmental concerns throughout the world, many new power sources, such as renewable energy, are being introduced. In order to maintain the reliability and stability of the system while integrating these inputs, many systems have started integrating battery energy storage systems. However, the introduction of such system has been very slow due to its high capital costs. Therefore, the economic benefit of a lithium ion battery energy storage system used for frequency regulation in a utility company is analyzed. The profit of a utility is calculated in terms of reduced amount of power purchase cost, and the results verify that the utility is able to recover their investments within the life cycle of the storage system in all cases.

The increasing penetration of plug-in electric vehicles (PEVs) may cause a low-voltage problem in the distribution network. In particular, the introduction of charging stations where multiple PEVs are simultaneously charged at the same bus can aggravate the low-voltage problem. Unlike a distribution network operator (DNO) who has the overall responsibility for stable and reliable network operation, a charging station operator (CSO) may schedule PEV charging without consideration for the resulting severe voltage drop. Therefore, there is a need for the DNO to impose a coordination measure to induce the CSO to adjust its charging schedule to help mitigate the voltage problem. Although the current time-of-use (TOU) tariff is an indirect coordination measure that can motivate the CSO to shift its charging demand to off-peak time by imposing a high rate at the peak time, it is limited by its rigidity in that the network voltage condition cannot be flexibly reflected in the tariff. Therefore, a flexible penalty contract (FPC) for voltage security to be used as a direct coordination measure is proposed. In addition, the optimal coordinated management is formulated. Using the Pacific Gas and Electric Company (PG&E) 69-bus test distribution network, the effectiveness of the coordination was verified by comparison with the current TOU tariff.

Under a deregulated environment, wind power producers are subject to many regulation costs due to the intermittence of natural resources and the accuracy limits of existing prediction tools. This paper addresses the operation (charging/discharging) problem of battery energy storage installed in a wind generation system in order to improve the value of wind power in the real-time market. Depending on the prediction of market prices and the probabilistic information of wind generation, wind power producers can schedule the battery energy storage for the next day in order to maximize the profit. In addition, by taking into account the expenses of using batteries, the proposed charging/discharging scheme is able to avoid the detrimental operation of battery energy storage which can lead to a significant reduction of battery lifetime, i.e. , uneconomical operation. The problem is formulated in a dynamic programming framework and solved by a dynamic programming backward algorithm. The proposed scheme is then applied to the study cases, and the results of simulation show its effectiveness.

Electric power transmission utilities try to maximize profit by reducing electricity supply costs and operation costs while maintaining their reliability. Developing maintenance strategies is one of the effective ways to achieve these profitable goals. The reliability-centered maintenance approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs of reliability losses. Since a transmission system is a group of different kinds of equipment and the reliability of the electric facilities varies with time, an equipment state model using a modified semi-Markov chain is proposed. In addition, a genetic algorithm is used to find the optimal maintenance strategies from a large class of possible maintenance scenarios. These optimal maintenance strategies have been tested on an IEEE 9-bus system and an IEEE 118-bus system; the results show that the proposed method minimizes the total expected costs.

DR(Demand response) is a rising issue in electricity power market by reducing load quantity instead of increasing generation for balancing demand and supply. DR resources are regarded as an extra tool of balancing supply and demand in emergency condition. DR resources usage in normal condition is also possible and used for the purpose of reliability and economic effect. For determining incentive for DR resources in PJM, fixed incentive level was used to reward the effect of market price reduction by DR program. However, since the fixed level of incentive is obtained by historical data, it cannot reflect the continuous change of market well. Furthermore, traditional method only concentrates to economic effect and it hardly concerns about the effect of preventing price variation by DR resources. In this paper, method of evaluating the market price reduction and prevention of price variation by DR is described under reliability considered situation. New mechanism for determining extra incentive is provided. From comparing the situation that DR is not implemented and the situation DR is implemented, novel mechanism can give more rewards to participants who contribute the stabilization of price and the reduction of load. Reward is exponentially increased as the DR resource stabilizes the price variation. On the other hand, reward is exponentially decreased as the DR resource increases price variation. Several cases are showed for checking effects and calculating the extra incentive.

In these days energy demand is dramatically increasing on the developed and developing countries. Even though the electric power efficiency is gradually improved, the limited energy resources and the shortage of land to construct generations have been existed for long time ago and the environmental limitation like the Kyoto-Protocol is originated. To overcome these obstacles, there is one solution through the cooperation on the national-wide. That is, we should meet the energy problem with having strong relationship among nations. The technological and systemic cooperation on electric power system is much more important than before. It is sure that there are economical and systemic advantages over the limitations when inter-regional power systems are operated well. Simply, power system interconnection gives the eco-friendly energy' applications and the flexible operating power system. In this paper, the first is focused on the algorithms on the power system and the other is based on the economic side. It is mainly to discuss the tertiary control illustrated over each first and secondary controls existing on two control areas.

Electricity customers can achieve economic benefits from cost reduction and/or revenue increase using battery energy storage system (BESS). However, different conditions of each customer require different methods to determine the suitable size of BESS. Additionally, different operation strategies of BESS result in the different cost/benefit analysis. Thus, in this study, authors have identified and focused on the electricity customers with TOU rate, varying operation mode with the seasons, and charging scheme based on both energy consumption and the peak demand. The procedures to find the optimal power and energy capacities for the selected customer are formulated. The optimal operation strategy with priority, that is, peak shaving–DR–scheduling modes in summer/winter and DR–scheduling mode–peak shaving modes in spring/autumn, is also proposed. The proposed method is applied to a specific type of customer in Korea in the case study. The results showed that an optimal capacity can be found and the proposed operation strategy with priority is optimal. Moreover, it has been found out that, for this type of customer, the large energy capacity of BESS mostly leads to large economic benefit; in contrast, the small power capacity mostly leads to small economic benefit.

Related to global efforts to reduce greenhouse gases, numerous electric vehicles (EVs) are expected to be integrated to the power grid. However, the introduction of EVs, particularly in Korea, is still marginal due to the lack of EV charging infrastructure, even though various supportive policies exist. To address this shortage of EV charging stations, the EV charging business needs to be profitable. As with any business, the profitability of the EV charging business is significantly affected by the initial capital investment related to EV chargers and auxiliary equipment such as power conditioning system (PCS), battery energy storage system (BESS), and on-site photovoltaic (PV) generation system. Thus, we propose a formulation to determine the number of EV chargers and the capacity of auxiliary equipment with the objective of a charging station operator (CSO) maximizing profit under regulatory, economic, and physical constraints. The effectiveness of the proposed method is verified with simulations considering various EV charging patterns. The study results will help improve the EV charging infrastructure by encouraging individuals and companies to participate in EV charging business.

The market share of electric vehicles is growing and the interest in these vehicles is rapidly increasing in industrialized countries. In the light of these circumstances, this study provides an integrated policy-making package, which includes key features for electric vehicle diffusion and its impact on the Korean power market. This research is based on a quantitative analysis with the following steps: (1) it analyzes drivers&rsquo, preferences for electric or traditional internal combustion engine (ICE) vehicles with respect to key automobile attributes and these key attributes indicate what policy makers should focus on, (2) it forecasts the achievable level of market share of electric vehicles in relation to improvements in their key attributes, and (3) it evaluates the impact of electric vehicle diffusion on the Korean power market based on an achievable level of market share with different charging demand profiles. Our results reveal the market share of electric vehicles can increase to around 40% of the total market share if the key features of electric vehicles reach a similar level to those of traditional vehicles. In this estimation, an increase in the power market&rsquo, s system generation costs will reach around 10% of the cost in the baseline scenario, which differs slightly depending on charging demand profiles.

This paper presents a decentralized adaptive backstepping controller to dampen oscillations and improve the transient stability to parametric uncertainties in multimachine power systems. The proposed design on the i th synchronous generator uses only local information and operates without the need for remote signals from the other generators. The design of the nonlinear controller is based on a modified fourth-order nonlinear model of a synchronous generator, and the automatic voltage regulator model is considered so as to decrease the steady state voltage error. The construction of both the control law and the associated Lyapunov function is systematically designed within the design methodology. A 3-machine power system is used to demonstrate the effectiveness of the proposed controller over two other controllers, namely a conventional damping controller (power system stabilizer) and one designed using the feedback linearization techniques.

The purpose of this paper is to examine the economic and technical perspectives of dynamically administered critical peak pricing (DA-CPP) program as an active demand response (DR) program. To implement a good DR program, there are three perspectives to be considered: regulatory, economic, and technical perspectives. We propose a special DA-CPP program as an effective DR program, which is administered by an energy service provider (ESP). ESP has control on when to call critical peaks on this program. This gives both the administrator and the end-use customers incentives to reduce loads on critical moments. We will assume that the regulatory perspective of DR is determined as a DA-CPP program and examine the other two. The economic perspective of DA-CPP program is the incentive of the program administrator, or the profit of an ESP. The technical perspective is a method to maximize the incentive of DA-CPP program, or an ESP's profit. An ESP should decide when to call critical peaks within certain constraints to maximize her profit. This is done by predicting the market prices and following a similar method as evaluating a swing option. The numerical example will show the optimal critical peak decisions. Copyright © 2008 John Wiley & Sons, Ltd.

The market power by large suppliers remains to be problematic in establishing an efficient competitive market for electricity. The exercise of market power is often facilitated due to a few notable properties of electricity as a tradable commodity. The pivotal supplier whose capacity exceeds the market's surplus of capacity above inelastic demand can strategically withhold its capacity and cause an undesirable price spike. This paper presents a methodology for determining an adequate amount of mandatory long-term contract to be used to mitigate the market power of the pivotal supplier and to eliminate the undesirable price spike. Using the Cournot model, the effect of mandatory long-term contracts is measured with respect to market equilibrium for the pivotal monopoly in which there is only one pivotal supplier and for the pivotal duopoly in which two suppliers join together and become pivotal. Applying the methodology described in the paper, the market efficiency is assured as the pivotal supplier that no longer has the incentive to cause the undesirable price spike. Copyright © 2007 John Wiley & Sons, Ltd.

In trying to determine the available transfer capability (ATC), this paper primarily sets out to develop a fuzzy logic approach to parallelizing contingency-constrained optimal power flow (CCOPF). This algorithm may be used by utilities to optimize economy interchange for severe contingencies analyzed without disclosing details of their operating costs to competitors. In fact, the ultimate objective of fuzzy multi-objective CCOPF (FMCCOPF) is to carry out the minimization of both the base case (pre-contingency) operating cost and the post-contingency correction times as conflicting but fuzzy goals. Besides, the Benders decomposition is applied to partition the fuzzy formulation with contingency constraints, which allows for post-contingency corrective rescheduling, motivated by the improvement of computational efficiency using parallel processing. The feasibility of the proposed method is comprehensively realized by a comparison with the conventional optimal power flow (OPF) and the CCOPF with respect to the same array of transactions, base case, and generator/line outages for the IEEE-30 bus system and the IEEE-118 bus system.

The operation of electric power network at any given time can be classified into one of the following states: normal, warning, alert, emergency and restoration, through reliability assessment. Typically, the classification into these states based on present method is useful for providing qualitative description of how reliably the network is being operated but lacks the quantitative valuation. In this paper we propose a new method for classifying operating states appropriate in Cost Based Pool (CBP) market in Korea. By taking the cost of control actions with respect to various plausible contingencies into consideration, the proposed method properly values the trade-off between technical reliability and economic efficiency. In applying the method the operational limits, such as maximum transfer capabilities and maximum voltages, are established at the planning stage. Established limits are then used to assess the reliability, and the control actions are applied by the system operator as needed at the operation stage. Several flowcharts are presented for showing how the proposed method is applied, and a case study is given for illustration purposes. It is shown that the proposed method properly incorporates the rational decision process by the system operator in detecting and responding to threats to system stability. In addition, it is shown that the classification by the proposed method is particularly useful for compensating network users participating in network control in the market environment.

The variability and uncontrollability of wind power increases the difficulty for a power system operator to implement a wind power system with a high penetration rate. These are more serious factors to consider in small and isolated power systems since the system has small operating reserves and inertia to secure frequency and voltage. Typically, this difficulty can be reduced by interconnection with another robust power system using a controllable transmission system such as a high-voltage direct current (HVDC) system. However, the reliability and stability constraints of a power system has to be performed according to the HVDC system implementation. In this paper, the method for calculation of maximum wind power penetration in an island supplied by a HVDC power system is presented, and the operational strategy of a HVDC system is proposed to secure the power system reliability and stability. The case study is performed for the Jeju Island power system in the Korean smart grid demonstration area.

Within active distribution network management, distributed generations, which are contracted by the non-firm connection method, can be directly controlled by the distribution network operator to adjust their power factor and the active power output level. This is called an output curtailment of distributed generation. Although the purpose of the output curtailment is to stably operate the distribution network, it inevitably limits the revenue of distributed generations. Therefore, the need for suitable compensation to the output curtailment is often discussed. As an extension to the discussion, this study proposes the method to determine the suitable bounds of the compensation price for output curtailment. In the proposed method, the suitable bounds of compensation price are obtained from the profit analysis of stakeholders in accordance with the level of the compensation price through optimal power flow technique.

This paper scrutinized the damping effects of installing the prototype PSSs by a transient analysis for eight buses of faults in the South Korean power system. The PSSs used have the ω-PSS blocks for IEEEST model with a single input and the ω+power PSS blocks for PSS2A model with dual inputs. The simulation tool was a TSAT (Transient Security Assessment Tool) developed by Powertech Labs Inc. The voltages of the transmission line for simulations were 765㎸ and 345㎸, and the faults for eight cases were sequenced by considering the open state and the close state of the lines. In the simulations, the three-phase line to ground (L-G) fault generated different points for each region. The simulations were compared to the cases of no PSS, partial IEEEST and PSS2A, absolute IEEEST, and absolute PSS2A to show that the power system oscillation can be effectively damped by PSS modules. Simulations were conducted to confirm the effectiveness for the KEPCO (Korea Electric Power Corporation) power system.