of the way various research activities contribute the enhancement of seismic resilience. And then, by measuring the four properties quantitatively, comprehensive
coordination of those researches can be achieved.
Energy model analysis using mathematical programming is an effective tool
measuring 4Rs (robustness, redundancy, resourcefulness, and rapidity) quantitatively. Then, in this paper, the authors try to present quantitative analytical
framework to discuss possible appropriate measures to implement seismic resilience
into electricity supply system, using simple energy model analysis. The energy
model used in this paper is a dynamic power generation planning model, which
incorporates the modelling of some measures in accordance with 4Rs and assesses
optimal capacity expansion strategy under successive nuclear power plants’
shut-down risk. It must be understood that the output of the energy model analysis
should not be like the future prediction. Its major concern is not to forecast a likely
future image of the energy system, but rather to derive a normative future image of
the system through the comprehensive incorporation of forecasted future parameters
and scenarios. The goal of this paper is to stimulate discussions about seismic
resilience enhancement by the normative image obtained through energy model
analysis and present its usefulness in that it can provide quantitative suggestions.
2 Dynamic Power Generation Planning Model
2.1 Mathematical Formulation
The dynamic power generation planning model mathematically expresses nuclear
power plants’ shut-down risk as yearly stochastic transitions of nuclear power
plants availability, and identifies the optimal capacity expansion strategy under the
uncertainty of the risk. This strategy is denoted by stochastic dynamic programming
[Eqs. (1) and (2)] and minimizes the expected total system cost necessary from y to
the expiration of an analytical period. This formulation highlights that it can consider the uncertainty of both nuclear power plants’ shut-down and recovery from
their disruptions, so it can express the preparation for risk and adaptive methods to
disruptions, which corresponds to redundancy and resourcefulness of the system.
As an analytical period, this paper assumes form 2012 to 2030, and power plants
considered are thermal power (coal, LNG steam turbine (ST), LNG combined cycle
(CC) and oil), nuclear power, hydro power including pumped type, and stationary
sodium-sulfur battery. Exogenous variables about them are based on [2], and fuel
price is set based on [3]. Concerning the installed capacity of coal-fired power
plants, LNG ST power plants, oil power plants, nuclear power plants and hydro
power plants, the maximum upper limit is assigned due to political, geographical or
some other reasons. Regional scope is the whole region of Japan and the electricity
market is assumed as a monopoly market. Annual power demand is expressed by
four representative load curves of each season in 2012, and it does not change until
2030. Problem formulation is described as follows.
Evaluation of Optimal Power Generation Mix …
291
coordination of those researches can be achieved.
Energy model analysis using mathematical programming is an effective tool
measuring 4Rs (robustness, redundancy, resourcefulness, and rapidity) quantitatively. Then, in this paper, the authors try to present quantitative analytical
framework to discuss possible appropriate measures to implement seismic resilience
into electricity supply system, using simple energy model analysis. The energy
model used in this paper is a dynamic power generation planning model, which
incorporates the modelling of some measures in accordance with 4Rs and assesses
optimal capacity expansion strategy under successive nuclear power plants’
shut-down risk. It must be understood that the output of the energy model analysis
should not be like the future prediction. Its major concern is not to forecast a likely
future image of the energy system, but rather to derive a normative future image of
the system through the comprehensive incorporation of forecasted future parameters
and scenarios. The goal of this paper is to stimulate discussions about seismic
resilience enhancement by the normative image obtained through energy model
analysis and present its usefulness in that it can provide quantitative suggestions.
2 Dynamic Power Generation Planning Model
2.1 Mathematical Formulation
The dynamic power generation planning model mathematically expresses nuclear
power plants’ shut-down risk as yearly stochastic transitions of nuclear power
plants availability, and identifies the optimal capacity expansion strategy under the
uncertainty of the risk. This strategy is denoted by stochastic dynamic programming
[Eqs. (1) and (2)] and minimizes the expected total system cost necessary from y to
the expiration of an analytical period. This formulation highlights that it can consider the uncertainty of both nuclear power plants’ shut-down and recovery from
their disruptions, so it can express the preparation for risk and adaptive methods to
disruptions, which corresponds to redundancy and resourcefulness of the system.
As an analytical period, this paper assumes form 2012 to 2030, and power plants
considered are thermal power (coal, LNG steam turbine (ST), LNG combined cycle
(CC) and oil), nuclear power, hydro power including pumped type, and stationary
sodium-sulfur battery. Exogenous variables about them are based on [2], and fuel
price is set based on [3]. Concerning the installed capacity of coal-fired power
plants, LNG ST power plants, oil power plants, nuclear power plants and hydro
power plants, the maximum upper limit is assigned due to political, geographical or
some other reasons. Regional scope is the whole region of Japan and the electricity
market is assumed as a monopoly market. Annual power demand is expressed by
four representative load curves of each season in 2012, and it does not change until
2030. Problem formulation is described as follows.
Evaluation of Optimal Power Generation Mix …
291