Shoichi Kitamura

An Energy Distribution Decision Method in Distributed Energy Management Systems by the Market-Oriented Programming

Control of CO2 emissions which is the main factor of global warming is one of the most important problems in the 21st century about preservation of earth environment. Therefore, efficient supply and use of energy are indispensable. We have proposed distributed energy management systems (DEMSs), where we are to obtain optimal plans that minimize both of costs and of CO2 emissions through electrical and thermal energy trading. A DEMS consists of the plural entities that seek their own economic profits. In this paper, we propose a trading method that gives competitive equilibrium resource distribution by applying the market-oriented programming (MOP) to DEMSs

Project scheduling under partially renewable resources and resource consumption during setup operations

A RCPSP formulation that can deal with realistic energy constraints is proposed.An integer program and a constraint program of the RCPSP are presented.A method to restrict selectable modes is proposed.Results of computation evaluation reveal show effectiveness of the proposed method. There has been an increasing pressure on manufacturing industries to reduce energy consumption. In this study, we propose a new variant of RCPSP called RCPSP/πRC, which can deal with realistic energy constraints such as power restriction during peak hours, contract demand, and energy consumption during setup operations. First, we present an integer programming (IP) model and a constraint programming (CP) model of the RCPSP/πRC. Next, we present a heuristic mode restriction method called a mask calculation algorithm to achieve efficient searching by restricting selectable modes. Finally, through computational experiments, we evaluate the proposed methods and show their effectiveness.

Solving Distributed Unit Commitment Problem With Walrasian Auction

In the process of scheduling multiple generating units, an up/down decision for every generating unit has to be made for every hour on the planning horizon. Once the unit commitment is decided for every generating unit, a generation level (economic dispatch) from the committed unit is calculated to minimize the total operation cost. Such a problem is known as a unit commitment problem (UCP). Because an up/down pattern is expressed with a vector of binary variables, one has to solve a combinatorial optimization problem. Due to electricity liberalization or the introduction of distributed power sources in recent years, the need to solve the UCP by autonomous distributed agents, that is, solving distributed UCP (DUCP), comes about. This paper proposes a method of solving the DUCP with a Walrasian auction. The main feature of the proposed method is that not only a generation-level pattern but also an up/down pattern can be determined. The results of computational experiments show that the DUCP is solved through the cooperation of autonomous distributed agents by using the proposed method.

Risk Based Capacity Planning Method for Semiconductor Fab with Queue Time Constraints

It is difficult to plan capacities of a semiconductor wafer fabrication with constraints of queue time which means a waiting time from the end of a step to the start of the next step for warranting production quality. If the queue time constraints could not be kept, the product is scrapped or reprocessed for qualitative recovery, and production efficiency remarkably decreases. This paper proposes an evaluation method which enables capacity planning of a fab with the queue time constraints, and shows its validity by simulation experiments. Furthermore we present an effectiveness of the method by applying to actual production lines.

An Energy Distribution Decision Method in Distributed Energy Management Systems with Several Agents

Abstract The need to control CO 2 emissions, which are the main factor of global warming, is one of the most important problems in the 21st century. Therefore, the efficient supply and use of energy are indispensable. We have studied distributed energy management systems (DEMSs), in which we target to optimal plans that minimize costs through electrical and thermal energy trading under CO 2 emissions regulation. Previously, a trading method in which the Market Oriented Programming (MOP) is applied to DEMSs was proposed. However, this trading method can be used in the DEMSs consisted of a single consumer and several producers. In this paper, extending this method, we propose a trading method that can be used in DEMSs which consist of several consumers and several producers. Experimental results show that the method is effective in the DEMSs with several consumers and several producers.

Computational evaluation of a market-oriented programming method for distributed energy management systems

Control of CO2 emissions which are the main factor of global warming is one of the most important problems in the 21st century about preservation of earth environment. Therefore, efficient supply and use of energy are indispensable. We have proposed a distributed energy management systems (DEMS), which consists of plural self-interested entities and in which they are to obtain optimal energy consumption plans that minimize cost under CO2 emissions constraints through electricity and heat trading. We have proposed an energy trading decision method by using the Market-Oriented Programming (MOP). This article evaluates the proposed method by computational experiments.

Disconnection detection method for power distribution lines using smart meters

For future power outage management of a power distribution system, a smart meter is expected to be used. Disconnection of a high-voltage power distribution line during a power outage runs the risk of a major disaster, so it needs to be detected quickly. In this paper, we describe a power outage management system developed as a part of an in-house smart grid demonstration test for detecting disconnections of highvoltage power distribution lines. Then we propose and validate a disconnection detection method that does not need information of connection phases of pole transformers.

OPERATION OPTIMIZATION METHOD FOR FACTORY POWER GENERATION PLANT CONSIDERING PROBABILITY DISTRIBUTION

Factory power generation plants supply electric power and heat to the manufacturing lines. Because the demand changes are uncertain, the power plant must adjust its output to match the demands and to avoid the violation of constraints such as the inverse load flow. Conventionally, these violations have been avoided by purchasing surplus electric power. However, the surplus power raises the energy cost. There exists an optimization method, which considers the uncertainty in the demands for co- generation systems, and implements a triangle function to approximate its probability distribution. However, this method does not consider the tail of the probability distribution. In this paper, an operation optimization method considering an uncertainty in the demands for factory power plants is proposed. The proposed method aims to avoid violations of constraints and to reduce the energy cost. Through simulation experiments, the validity of the proposed method is confirmed.