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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.

Smart contract is an important building block of blockchain. Automated market makers are working without an order book, and they determine the price of assets automatically. It is reported that he automated market makers have the impermanent loss, which causes financial damage to liquidity providers. Impermanent loss makes the liquidity providers hesitant to deposit assets in the liquidity pool. Therefore, their participation incentive from liquidity provision should be anticipated by automatic market makers inherently. However, the existence of impermanent gain has never been reported. Impermanent gain is important to attract liquidity providers without giving compensation incentives. This study shows that for some automated market makers, impermanent gain coexists with impermanent loss. Examples showing the coexistence and conditions are provided.

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.

Variability, intermittency, and limited controllability are inherent characteristics of photovoltaic (PV) generation that result in inaccurate solutions to scheduling problems and the instability of the power grid. As the penetration level of PV generation increases, it becomes more important to mitigate these problems by improving forecasting accuracy. One of the alternatives to improving forecasting performance is to include a seasonal component. Thus, this study proposes using information on extraterrestrial radiation (ETR), which is the solar radiation outside of the atmosphere, in neural network models for day-ahead PV generation forecasting. Specifically, five methods for integrating the ETR into the neural network models are presented: (1) division preprocessing, (2) multiplication preprocessing, (3) replacement of existing input, (4) inclusion as additional input, and (5) inclusion as an intermediate target. The methods were tested using two datasets in Australia using four neural network models: Multilayer perceptron and three recurrent neural network(RNN)-based models including vanilla RNN, long short-term memory, and gated recurrent unit. It was found that, among the integration methods, including the ETR as the intermediate target improved the mean squared error by 4.1% on average, and by 12.28% at most in RNN-based models. These results verify that the integration of ETR into the PV forecasting models based on neural networks can improve the forecasting performance.

Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

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.

Interconnection of power systems is one effective way to improve power supply reliability. However, differences in the reliability of each power system create a greater obstacle for the stable interconnection of power systems, as after interconnection a high-reliability system is affected by frequent faults in low reliability side systems. Several power system interconnection methods, such as the back-to-back method and the installation of either transformers or series reactors, have been investigated to counteract the damage caused by faults in the other neighboring systems. However, these methods are uneconomical and require complex operational management plans. In this work, a high-speed trigger-type superconducting fault current limiter (TSFCL) with large-impedance is proposed as a solution to maintain reliability and power quality when a high reliability power system is interconnected with a low reliability power system. Through analysis of the reliability index for the numerical examples obtained from a PSCAD/EMTDC simulator, a high-speed TSFCL with a large-impedance is confirmed to be effective for the interconnection between power systems with different reliabilities.

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.

A microgrid can be a useful entity to support stable and efficient operation of power systems with large-scale penetration of distributed generators. Suppose that a microgrid should provide reserve operation in a decentralized environment; determining a suitable reserve capacity becomes an important problem for reliable and economic operation. To address this problem, in this paper we present a method of determining the optimal reserve capacity under the power exchange for frequency control (PXFC) market environment. After creating some penalty rules that take microgrid islanding into account, determining the optimal reserve capacity is formulated as an optimization problem. Microgrid islanding probability (MIP) is proposed as an index to describe the likelihood of microgrid islanding. The effectiveness of the method in terms of the operating cost is investigated using simulations, where our method and two further methods are applied to microgrids with different generation capabilities. The properties of MIP are also demonstrated by the simulations.

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.

Investment options of transmission expansion planning (TEP) involve different lead times according to their length, technology, and environmental and social impacts. TEP planners can utilize the various lead times to deal with the risk of uncertainty. This paper proposes a novel framework for TEP under an uncertain environment, which includes investment options with various lead times. A multi-stage model is developed to reflect the different lead times in the planning method. The level of demand uncertainty is represented using a relative standard deviation. Demand uncertainty in the presented multi-stage model and its influence on the optimal decision are studied. The problem is formulated as a mixed integer linear problem to which stochastic programming is applied, and the proposed framework is illustrated from case studies on a modified Garver&rsquo, s six-bus system. The case studies verify the effectiveness of the framework for TEP problems with a mathematically tractable model and demonstrates that the proposed method achieves better performance than other methods when the problems involve investment candidates with various lead times under uncertain conditions.

Critical peak pricing (CPP) is a demand response program that can be used to maximize profits for a load serving entity in a deregulated market environment. Like other such programs, however, CPP is not free from the payback phenomenon: a rise in consumption after a critical event. This payback has a negative effect on profits and thus must be appropriately considered when designing a CPP scheme. However, few studies have examined CPP scheme design considering payback. This study thus characterizes payback using three parameters (duration, amount, and pattern) and examines payback effects on the optimal schedule of critical events and on the optimal peak rate for two specific payback patterns. This analysis is verified through numerical simulations. The results demonstrate the need to properly consider payback parameters when designing a profit-maximizing CPP scheme.

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 deregulated market environment in power industries offers utilities or load serving entities the chance to make profit by pursuing a suitable operational strategy. However, the volatility of the real-time market clearing price raises a price risk issue because the load serving entity sells electricity to customers at a relatively frozen retail rate. One method to hedge price risk is to implement various dynamic pricing schemes in the retail sector in order to reflect the volatility of the real-time market clearing price to the retail rate. This paper presents several analyses for designing one such pricing scheme, namely critical peak pricing for a profit-maximizing load serving entity. Specifically, how the parameters of critical peak pricing affect profit based on the price responsiveness model of customers is analyzed. In this process, a method for solving the events scheduling problem is used as a tool for the analyses. Furthermore, we offer intuitive guidelines and rules for selecting those parameters that maximize the profit of the load serving entity. Finally, the suitability and practicality of the presented analyses are verified by numerical simulations with forecasted data on the real-time market clearing price and demand.

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.

This paper discusses a method for the determination of frequency control reserve requirement with consideration of the interaction between ex-ante planning and real-time balancing. In proposed method, we consider the fact that the delivered energy for tertiary control reserve is determined based on required capacity for secondary control reserve and the expected amount of load errors. Uncertain load errors are derived by Brownian motion, an optimization method is suggested using a stochastic programming. In a short, we propose an interactive dependent method for determining secondary control reserve requirement based on the principle that it satisfies to minimize the total cost. As a result, this paper provides will analyze for an example model to demonstrate the capabilities of the method.