The vehicle electrification, the replacement of engine vehicles by hybrid (HV), electric (EV) and fuel cell vehicles (FCV) is expected to reduce CO₂ emissions in transport sector. However, the reduction amount by EV and FCV is largely affected by CO₂ emission intensity of power system and the way to make hydrogen. Authors simulate a power system in 2030 where a large amount of PV is implemented, and PV generated energy must be curtailed on many days. The annual cost is minimized by optimizing the hourly output of coal fired and LNGCC plants, when the 16% of passenger vehicle mileage is replaced by EV charged at midnight and/or daytime. In FCV case, the capacity of water electrolysis and hydrogen tank, and the hourly electrolysis output as well as thermal power plant output are optimized. Results show CO₂ emissions decrease particularly when EVs are charged at daytime and the charging power is controlled to contribute frequency stability. The electrolysis demand decreases the PV energy curtailment but increases the CO₂ emission because of the lower energy converting efficiency and higher facility cost. The energy chain including whole power system, demand and fuel is analyzed to see the power flow and CO₂ emission.

<p>This paper presents economic evaluation of storage battery systems which increase the self-use of PV output at houses and buildings. The selling price of PV generated energy is decreasing. Battery energy storage systems (BESS) can increase the self-use of PV output, and decrease the volume of buying electric power. So, the introduction of BESS to houses and buildings equipped with PV systems will be economical when the price of BESS is decreased enough. Authors investigated the optimum BESS capacity and hourly charge-discharge pattern for a year, then evaluated annual cost. Daily load pattern of each house and building, and time-of-use electric rates affect the conditions to make BESS cost-effective. Such conditions are shown in this paper.</p>

This paper presents the change of CO₂ emission when battery energy storage systems (BESS) are used for increasing self-use of PV energy from rooftop panels, and for decreasing PV output curtailment. A traditional evaluation uses an annual CO₂ emission intensity and the charge-discharge loss is evaluated, and then the emission is increased. However, when PV system is largely penetrated and the PV energy curtailment is required by power system constraints, BESS can decrease the curtailment as well as the evening demand when the rate of thermal power is large, and increase the daytime demand when the rate of PV energy is high. So, BESS can decrease CO₂ emission considering hourly CO₂ emission intensity. Authors quantify these effects by use of an optimization model to minimize annual power generation costs, and a model to minimize annual costs of houses with PV and BESS, and then shows energy chain diagram including energy flow between generation plants and customers. The CO₂ emissions from business use customers with PV and BESS, and power charging demand for EV are also evaluated using hourly CO₂ emission intensity. Authors show BESS can decrease CO₂ emission.

We evaluated power generation system costs in Japan in 2030 in cases of different ratios of renewables and nuclear. We conducted simulations using a power generation mix model and obtained the following results. When the ratio of the renewables increase, the average unit cost of the overall power generation systems will increase because of not only the high unit costs of renewables but also the additional adujutment costs so as to adjust the supply and demand of the power including the outputs of variable renewables. The costs of the power generation systems that include overall renewables at 21%, 30%, and 40% of the total generation will be 1.2, 1.4 to 1.6, and 1.7 to 2.3 times, respectively, compared with the reference (1.0) of the unit cost of the power systems that includes overall renewables at 13% but include no varibable renewables.

Since photovoltaic and wind generate fluctuated power, the large scale introduction requires additional load frequency control (LFC) capacity. LFC is supplied by pumped hydro and thermal power plants in partial load operation, and requires more fuel and cost. The authors developed Multi-mode Optimum Power Generation Mix Model which can deal with the LFC constraint and efficiency drop when thermal power and pumped hydro plants operate at partial load. The simulation results show that large scale introduction of renewable energy increase the cost of other power plants because of the load factor decrease of thermal power and the increased operation of pumped hydro power.

Since photovoltaic and wind generate fluctuated power, the introduction may require additional load frequency control (LFC) capacity and make fossil-fired power plants operate partial load at lower efficiency. The purpose of this study is to develop an optimal power generation mix taking into account LFC capacity and multiple operation modes of thermal and pumped hydro power plants. For that purpose, the authors developed a Multi-Mode Optimal Power Generation Mix Model (MM-OPG) and evaluated large-scale penetration of renewables in Japan.

The load forecasting of the electric power has been studied extensively in the world. In many cases, the load forecast of the electric power systems is studied in the electric power company rather than demand side. The load forecast in the demand side is affected by some specific factors that are not measured and expected in advance. Although the factors/parameters are limited, it was difficult to build standard models that are applicable to various demand sides. In the era of deregulation, however, the management of customers' load became more important. Moreover, the demand forecast with high accuracy is necessary for the proper operation of BESS (battery energy storage system) such as NAS battery.
In this paper a load forecasting technique using multiregression model is proposed. Based on five years' records of the load in a university campus, the load for tomorrow can be forecasted. Numerical tests demonstrate the robustness and accuracy of the proposed technique.

The slowdown in power demand increase and facility replacement causes the aging and lower reliability in power facility. And the aging is followed by the rapid increase of repair and replacement when many facilities reach their lifetime in future. This paper describes a method to estimate the repair and replacement costs in future by applying the life-cycle cost model and renewal theory to the historical data. This paper also describes a method to decide the optimum investment plan, which replaces facilities in the order of cost-effectiveness by setting replacement priority formula, and the minimum investment level to keep the reliability. Estimation examples applied to substation facilities show that the reasonable and leveled future cash-out can keep the reliability by lowering the percentage of replacements caused by fatal failures. © 2008 The Institute of Electrical Engineers of Japan.

Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models. © 1995 IEEE.