Akihiro Yoza

A study on optimum capacity of battery energy storage system for wind farm operation with wind power forecast data

In order to solve problems of global warming and depletion of energy resources, renewable energy systems such as wind generation is getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to wind power forecast perfectly. This paper describes an optimal operation method of wind farm (WF) taking into account wind power forecast error. The optimization purpose is to smooth the output power fluctuation of the WF and to obtain more benefit for electrical power selling. Because wind power developers should follow the strict condition for output power smoothing. The power fluctuations of the wind power are smoothed by using battery energy storage systems (BESS) in almost power systems. BESS utilizes to smooth the power fluctuation and to compensate surplus power or shortage power due to uncertainty of wind power forecast. However, integrating large batteries with the wind plants as a result increase cost for the power system, thus, optimization of capacity of the BESS is necessary. This paper shows optimum capacity of the BESS using an operation method taking into account the wind power forecast error.

Optimal Operation Method of Smart House by Controllable Loads based on Smart Grid Topology

Abstract From the perspective of global warming suppression and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all electrification apartment house or residence such as DC smart house have increased in recent years. However, due to fluctuating power from renewable energy sources and loads, supply–demand balancing fluctuations of power system become problematic. Therefore, “smart grid” has become very popular in the worldwide. This article presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuations. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuations, it is possible to reduce the maximum electric power consumption and the electric cost. This system consists of photovoltaics generator, heat pump, battery, solar collector, and load. In order to verify the effectiveness of the proposed system, MATLAB is used in simulations.

Optimal Operation Strategy with using BESS and DGs in Distribution System

This paper proposes a methodology for distribution system operation using battery energy storage system (BESS), interfaced inverter with DGs and existing voltage control devices. The proposed method decides the optimal operation schedule of each control device by optimal calculation. Optimal calculation uses a forecast information for a next day photovoltaic generator output and load demand. As a result, the proposed method achieves a voltage control for each of the node within the acceptable range, smoothing an interconnection point power flow and reducing of total distribution loss. The effectiveness of the proposed method is verified by using MATLAB.

Optimal operation of controllable loads in DC smart house with EV

From the perspective of global warming suppression and depletion of energy resources, renewable energies, such as solar collector (SC) and photovoltaic generation (PV), are getting attention in distribution systems. Then, all electric houses with PV and HP are becoming widely used recently because of using time-of-day electricity rate system. If fixed battery can be introduced in the houses, the electricity cost would be more reduce. Moreover, electric vehicle (EV) is coming up in utilization. Therefore, home energy management system (HEMS) is needed for optimization of the coordinated operation of these equipments. In this paper, we propose that the all electric smart house with introduction of fixed battery and EV in addition of PV, HP and SC, where, HP, fixed battery and EV are used as controllable loads. Hence, optimal operation planning of these equipments is decided for the purpose of minimizing the operational cost during a day. We confirmed the proposed method is useful, and in order to verify the effectiveness of the proposed system, MATLAB® is used for simulations.

Optimal scheduling method of controllable loads in smart house considering forecast error

From the perspective of global warming suppression and depletion of energy resources, renewable energies, such as the solar collector (SC) and photovoltaic generation (PV), are getting attention in distribution systems. Houses with PV and heat pump (HP) systems are also gaining wide use recently in residential areas recently because of using time-of-use electricity pricing. Thus, optimal scheduling of the distributed energy resources (DER) is required to reduces the operational cost for the consumers, however DERs of then deviate from forecast data. Hence, optimal scheduling of these appliances may not be achieved due to uncertainty of the weather. In this paper, we propose an optimal scheduling method of controllable loads considering forecast error in the smart house, where HP, fixed battery and EV systems are used as controllable loads. The scenario based approach is applied in roder to solve the optimization problem involving the uncertainty. Furthermore, a novel electricity pricing system is also suggested in this paper. In order to confirm the proposed method is useful, and in to verify the effectiveness of the proposed system, MATLAB® is used for simulations.

Optimal scheduling method of controllable loads in DC-smart house with deregulated electricity market

The price of electricity has especially become expensive due to energy shortage with respect to the halting of nuclear power generation during peak hours in Japan. It is thought that a deregulated electricity market not only has a real chance to be an effective contribution to the reduction of price, but also a method for the efficient operation of thermal generators. From this background, this paper presents a method for optimal operation scheduling of controllable loads in the residential sector considering the environment of the electricity market with an aggregator who coordinates consumer demand for power receiving requests from the utility company. An optimization problem to determine the optimal price traded between the aggregator and consumers, and scheduling of controllable loads, are solved by proposed method. The simulation results indicate that a minimization of operational cost on both sides and a decrease of peak power demand are achieved by using the electricity market.

Comparison of operational cost in smart houses with introduction of heat pump or gas engine

From the perspective of global warming suppression and depletion of energy resources, renewable energies, such as wind generation (WG) and photovoltaic generation (PV), are getting attention in distribution systems. Then, electric energy saving instruments, such as heat pump (HP), contribute to low carbon society and are good for consumer also. However, gas instruments, such as gas engine (GE) and fuel battery, are coming up. So, it is important to verify which smart houses, with heat pump or gas engine, are more beneficial for consumer. This paper discuss which smart house, with HP or with GE, is better in operational cost for consumer in the demand side. Where, we used current electricity and gas prices to calculate cost of smart house. The simulation results, during each month, proved that the houses using electrical energy saving instruments have the cheapest operational cost. The system considered in this research consists of PV, battery, solar corrector (SC), HP or GE and load. In order to verify the effectiveness of the proposed system, MATLAB® is used for simulations.