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The service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two‐stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed‐integer linear programming problem in the second stage. The computational experiments using a large‐scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper summarizes our recent work on Grid‐Forming Inverter (GFM) application to power systems. We have developed a novel design of GFM, a single‐phase synchronous inverter (SSI) for the conventional 100/200V distribution network based on the concept of "non‐interference core (NIC) dynamic model." This paper first explains the design concept of NIC‐SSI and a simulation model of SSI for power system analysis. Then the stabilization effect is investigated, where the installation of SSI on the single‐phase consumer side is assumed as a new concept. The improvement of frequency and transient stability are evaluated. The SSI model is verified by comparing the developed root mean square (RMS) simulation, hardware‐in‐the‐loop (HIL) simulation, and the experiment using SSI hardware. The simulation results show that the SSI has the considerable ability of grid stabilization. Singlephase micro‐grid (SMG) operations using SSIs are also presented. The effectiveness of SMG operations is shown based on laboratory experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a controller for single‐phase synchronous inverters (SSIs) that was designed to stabilize the performance of a grid while providing overcurrent protection during the occurrence of a fault. The proposed controller for an SSI eliminates the multiple feedback control loops consisting of voltage and current controllers under normal conditions to avoid the instability attributed to them. To output the designed voltage precisely calculated using the core circuit, including a swing equation and speed governor, we employ the latched limit strategy for the overcurrent protection, which is combined with a proportional resonance current controller. Numerical simulations and experiments demonstrated that the proposed controller provides the designed performance while ensuring overcurrent protection during normal operation. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A novel design of a single‐phase synchronous inverter (SSI) having noninterference core dynamic performance is proposed, referred to as noninterference core SSI (NIC‐SSI) in this paper. The proposed inverter consists of "core" and "shell." This novel design is advantageous for a flexible setting of the core controller to enhance power system stability, which is supported by the shell function for keeping robust synchronizing operation of inverters including microgrid operation. The novel points in this paper are summarized as: (a) the proposed controller enables SSIs to implement directly a desired dynamic characteristic such as a synchronous machine; (b) it can connect any single‐phase systems as well as three‐phase systems; (c) an islanded single‐phase microgrid without rotating machines can be stabilized by multiple SSIs. The proposed controller makes it possible to enhance system stability against increasing uncontrollable renewable energy sources (RESs) and static power conversion devices. The developed SSI realizes flexible use in the demand side operations under normal/faulted grid conditions. Hardware‐in‐the‐loop (HIL) testbed including the proposed digital controller and the real‐time simulator validates the effectiveness of the proposed controller in the experimental conditions. [ABSTRACT FROM AUTHOR]

This paper provides a novel equilibrium model for analyzing behaviors of the electric retailers under competitive environment. In the deregulated electricity retail market, the retailers purchase the electricity power and sell it to consumers at the competitive prices. According to their risk attitudes, the retailers optimize the selling prices and the purchase allocation between a day‐ahead market and forward contracts. Without the regulation, an equilibrium by those selfish decision‐making processes of multiple retailers would cause an adverse impact on the electricity market and also the power system. From stable and economical viewpoints, it is important to analyze the unfavorable equilibrium. The selfish behaviors of retailers are modeled as an equilibrium problem with equilibrium constraints in this paper and formulated as a mixed integer linear programming problem to obtain a generalized Nash equilibrium by commercial solvers efficiently. Through computational examples, the proposed model and formulation are validated. [ABSTRACT FROM AUTHOR]

Highlights • The time-series rational response of the consumer is introduced. • The fractile criterion is employed on the non-convex decision making problem. • The bi-level programming problem is converted to MILP. Abstract This paper presents a novel decision making framework of time-of-use (TOU) price settings and procurement strategies in medium-term planning for an electricity retailer taking into account the rational responses of consumers to the TOU prices. Key ideas of this paper are (1) to introduce the time-series responses of the consumers to the time-varying TOU prices in the medium-term planning; and (2) to find a preferred solution reflecting the preference of the retailer to uncertainty caused by volatile electricity market prices and demand in the non-convex decision making problem. The details of the proposed method to realize the aforementioned key ideas are summarized as follows: (i) assuming the rational responses of the consumers to the time-varying TOU prices offered by the retailers including a decision maker and rival retailers, the decision making problem of the retailer to optimize both the TOU price settings and the purchase allocation in the multiple channels of purchase, i.e., mid-term forward contracts and a short-term day-ahead market, is modeled as the Stackelberg game; (ii) on the non-convex decision making problem formulated as the bi-level programming problem, the preference of the retailer to the uncertainty is reflected by employing the fractile criterion using the stochastic aspiration level specified by the retailer; and (iii) the bi-level programming problem is converted to the mixed integer linear programming problem (MILP), which can be solved by the commercial solver efficiently, by Karush-Kuhn-Tucker optimality conditions and the strong duality theorem. Through the computational experiments, we demonstrate validity of the proposed decision making framework and some findings revealed by introducing the rational response of the consumer. [ABSTRACT FROM AUTHOR]

This paper deals with two-level production and distribution planning problems in supply chain management where the leader is a distributor and the follower is a manufacturer. Assuming that the distributor can observe the input–output data in the production process, we formulated the data envelopment analysis (DEA) production problem corresponding to the production planning problem of the manufacturer. This paper proposes a novel data envelopment analysis (DEA) approach to solve a challenging multiperiod two-level production and distribution planning problem in supply chain management. The innovative idea behind the proposed approach is to allow the distributor to observe the input–output data regarding the production activities of the manufacturer, even if the distributor cannot fully comprehend all parameters of the manufacturer's production cost minimization problem. This approach addresses the challenge of uncertain demands by employing a two-stage model with simple recourse and considering the usage of the input–output data. The paper demonstrates the validity of the proposed DEA approaches through computational experiments and discusses the accuracy, reliability, and importance of the input–output data. The proposed approach provides a practical and effective solution to the multiperiod two-level production and distribution planning problem in supply chain management, and can help decision-makers improve the efficiency and effectiveness of their operations. The innovative idea of allowing the distributor to observe the input–output data about the production activities of the manufacturer is a significant contribution to the field of supply chain management and has the potential to advance the state of the art in this area. [ABSTRACT FROM AUTHOR]

This paper proposes a socially acceptable framework for the realization of efficient voltage management by cooperation among consumers in electric distribution systems where the frequent voltage variation problem is caused by large amounts of photovoltaic (PV) generation systems. In the proposed framework, the profit maximization problem of the coalition under the constraints to suppress the voltage variations caused by PVs is formulated as a linear programming problem, and the payoff allocation among consumers is provided using a solution concept from cooperative game theory called the core. The proposed method efficiently computes the payoff allocation in the nonempty core to accomplish the cooperative voltage management system. Unlike most related works, this paper employs cooperative game theory to explicitly address incentives for cooperation with the consumers. Although the computation for finding an allocation in the core generally requires a significant amount of time, this paper demonstrates that the allocation can be efficiently calculated by using the dual optimal solution of a formulated linear programming problem. The proposed method is validated by computational experiments on a large-scale distribution system model in which consumers with demand resources (including PVs and controllable loads) are connected. • The proposed method provides an allocation of the payoff belonging to the core. • Under the proposed framework, the cooperative actions by all consumers are stable. • The proposed core allocation requires a low computational burden. [ABSTRACT FROM AUTHOR]

This paper presents a novel electricity retail market model in which elastic demands of consumers in a distribution network are traded at flexible selling prices offered by a retailer. The main works of this paper are in three points: (1) Flexible and divided selling price settings over one day by the retailer, (2) flexible and elastic responses corresponding to the selling prices (price-based Demand Response: DR) by different types of consumers, and (3) distribution network physical constraints to obtain the realistic cost of tariff for usage of the distribution network imposed to the retailer are formulated. Unlike previous related works, the proposed model is a new one applicable to the behavior analysis of decision makers in the deregulated environment with such flexible transactions. In the transactions, the retailer offers a selling price for a unit time period over one day and the consumers elastically respond to the prices. Assuming that the consumers respond to the prices rationally and control their demands flexibly, we model the transaction as a Stackelberg game formulated by a bi-level programming problem. The behavior of the market players is examined in computational experiments using the spot price data in Japan Electricity Power eXchange (JEPX) and distribution network models, i.e. modified IEEE 13 bus test system model and IEEE 33-bus radial distribution test system model. We employ a genetic algorithm to find an approximated solution of the formulated non-convex bi-level programming problem. The computational results show some new findings about the deregulated retail market if flexible transactions between the retailer and the consumers are realized. [ABSTRACT FROM AUTHOR]

SUMMARY The liberalization movement in Japan will be expanded to the residential sector and full liberalization of the retail market will be achieved in the near future. Against this background, it is expected that transactions in a distribution network (DN), which has many consumers of the residential sector, will be activated. Furthermore, consumers can control their loads depending on the selling prices offered by retailers with the introduction of demand response (DR) technologies such as home energy management systems (HEMS). Due to the variation of load by DR, however, the voltage profile in DN may be changed compared to the present situation. This may make voltage management difficult and cause problems such as voltage deviation from an adequate range. In this paper, the impact of DR in a liberalized electricity market on DN is evaluated for efficient voltage management. In order to evaluate the behavior of the retailer and consumers, this paper proposes a bi-level programming approach based on the Stackelberg game model. Solving the bi-level programming problem including the power flow equation for a DN model based on the IEEE 13-bus test feeder, we analyze the impact of the retailer and consumers on the voltage in the DN in order to account for transactions between these market players. [ABSTRACT FROM AUTHOR]

We consider two-stage stochastic linear production planning problems with partial cooperation including resource pooling, technology transfer and product transshipment, and employ a two-stage programming model with simple recourse to address uncertain demands. At the first stage, each manufacturer individually determines the production level according to its own technology and transferred technologies. After the demands are realized, multiple manufacturers jointly produce the products using pooled resources, and surplus products are transshipped to manufacturers with residual demands. Using the core solution concept from cooperative game theory, the additional profit obtained at the second stage is divided among all manufacturers. We develop a method to find a Nash equilibrium point such that the sum of the profits earned by all manufacturers is maximized. To demonstrate the validity of the proposed model, numerical examples are presented. [ABSTRACT FROM AUTHOR]

A learning classifier system is an adaptive system that obtains a set of appropriate action rules that adapts to multistep problems by training action rules defined in if‐then form by trial and error process, in a similar framework as reinforcement learning. Because of that the input signals of the classifier system are encoded into binary values, bit strings are often lengthened when dealing with such a problem that the state of the environment continuously changes. A neural network can treat with real values as input signal; however, it cannot be applied to multistep problems. This paper proposes a system that responds to problems such that the state of the environment continuously changes by combining a neural network and a classifier system, and actions are selected from multiple options, so that output can be defined as discrete values. In order to verify the effectiveness of the proposed system, this paper conducts several numerical experiments using benchmarks corresponding to multistep problems defined by continuous values. [ABSTRACT FROM AUTHOR]

In this paper, we deal with group multiattribute utility analysis incorporating the preferences of multiple interested individuals. Since it is difficult to repeatedly ask these individuals questions for determining parameters of a multiattribute utility function, we gather preference information of them by asking questions that are not difficult to answer, and develop a method for selecting an alternative consistent with the preference information. Assuming that the multiattribute utility function has the multiplicative form and the corresponding single-attribute utility functions are already identified, we evaluate the trade-off between attributes by utilizing neural networks whose inputs and outputs are the preference information elicited from the interested individuals and the scaling constants for the multiattribute utility function, respectively. By performing computational experiments, we verify that the proposed method can generate scaling constants properly. Furthermore, using the preference relations from two groups with different degrees of the preferential heterogeneity, we examine the effectiveness of the proposed method, and then we show that the results of the numerical applications are reasonable and proper. [ABSTRACT FROM AUTHOR]