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Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

To promote the development of renewable energy, the Chinese Government adopts the policy of Feed-in Tariff and subsidy. However, the high purchase price and the intermittence limit the development of renewable energy source electricity (RES-E). The purpose of this paper is to discuss the pricing strategy for system operators to stimulate the development of the RES-E industry under the scenario of uncertain supply and demand.

The authors establish a two-echelon supply chain investment model led by a power grid operator considering the uncertainties in both demand and supply, and study the impact of the power purchase price designed by a system operator using Stackelberg’s model.

There is an optimal capacity for RES-E generators, that is, independent of the market demand. Besides, the optimal order of grid operators is independent of the uncertain RES-E supply and the purchase price of fossil fuel. By properly setting the purchase prices, the system operator can stimulate the capacity investment in renewable energy. Finally, increasing the punishment in power shortage can stimulate the capacity investment in RES-E under certain conditions.

The result of this paper can mitigate the phenomenon of power abandonment in the RES-E industry and promote the grid integration of RES-E.

Both uncertain demand and supply are considered in this paper. A heuristic algorithm is provided to compute the optimal purchase price combination.

The purpose of this paper is to present a decisions support tool that can be applied in initial stages of design, for evaluating the investment feasibility of changeable and reconfigurable manufacturing design concepts, based on future demand predictions and their uncertainties. A quantitative model is proposed, which evaluates the discounted value of capital and operating costs of changeable manufacturing design concepts, based on essential characteristics regarding their type and extent of changeability.

Quantitative empirical modeling is applied, where model conceptualization, validation, and implementation are central elements, using two Danish manufacturing companies as cases.

The applicability of the model is demonstrated in the two case companies, highlighting differences in type, extent, and level of feasible changeability, as a result of differences in product and production characteristics.

Further studies of changeability implementation should be conducted across industrial fields in order to generalize findings.

There is currently limited support for the conceptual design phase of changeable and reconfigurable manufacturing, where critical decisions regarding type, extent, and level of changeability must be made, regardless of high degrees of uncertainty about future demand scenarios.

This paper expands previous research on design for changeability and reconfigurability, by explicitly considering changeability as a capability that can be enabled in various ways for various purposes in different industrial contexts. The proposed model and the case implementations provide important knowledge on the transition toward changeability in industry.

How large should a distribution facility be initially? What size additions to the facility should be planned and when? For example, should a regional distribution centre be built to accommodate the projected inventory volume at the time of its completion or the projected volume some five, ten or fifteen years in the future? Should investments in the building and the materials handling system coincide or be planned separately? Finally, can planned expansions be economically postponed by temporarily leasing outside space or by temporarily expanding the work force to achieve above‐capacity utilisation of the existing facility?

When the power generator faces uncertain and independent electricity spot price and renewable energy source supply, two different conditions need to be considered: the distributions of renewable energy source electricity and electricity spot price are independent or dependent. The purpose of this paper is to explore the capacity investment strategy under volatile electricity spot price when renewable energy penetration rate is low, taking into account these two conditions.

The authors design a capacity investment model under dual uncertainties and consider how to optimize the investment capacity in order to maximize profit under two different conditions.

The authors find that when renewable energy supply fluctuation is unrelated to spot electricity price fluctuation, the renewable energy power profitability is determined by the average cost of spot electricity price and equivalent cost. When renewable energy supply fluctuation is related to spot electricity price fluctuation, the renewable energy power profitability is determined by the market value and the construction and maintenance cost.

Faced with the conflict of the renewable energy supply, the authors need to understand how to plan the generation capacity with intermittent renewable sources. The result helps renewable energy become competitive in the electricity market under loose regulations.

The authors compare two capacity investment strategies that the renewable energy supply fluctuation is related and unrelated to spot electricity price.

We address the problem of valuing electricity generation capacity and the opportunities to invest in power generation assets in the deregulated electric power industry. The spark spread option‐based valuation framework is extended to take into consideration the electricity price spikes. This framework provides a valuable tool for merchant power plant owners to perform hedging and risk management. With jumps in the value process of power generation capacity, we demonstrate how to determine the value of an opportunity to invest in acquiring the generation capacity and the threshold value above which a firm should invest. We illustrate the implications of price spikes on the value of electricity generating capacity and the investment timing decisions on when to invest in such capacity.

Process flexibility (PF) is seen as a hedging instrument against demand uncertainty. This paper aims to examine capacity decisions for both flexible and dedicated processes under production policies such as make-to-order and make-to-stock. The study identifies some relative benefits, in terms of expected profit, of the process flexible plant over the dedicated ones. Furthermore, the advantage appears to be contingent upon the decision on the preset service level.

Using the sample-based optimization procedure, a detailed computational analysis is undertaken to identify the conditions under which a flexible plant is preferred over a dedicated plant. A combination of genetic algorithm and sample-based optimization procedure is used to capture the effects of preset service level. The factors controlled in this paper include the demand variance, demand correlation, capacity investment cost and the product price.

According to this study, in a dedicated process changing to a flexible process is not justified for the same level of demand correlation even with high demand variance. In fact, a strict control on the preset service level prefers the dedicated strategy. The advantage of a flexible plant increases as the demand correlation decreases, product price decreases, price asymmetry increases or capacity investment cost increases. With a preset service level constraint, a flexible process should be preferred to a dedicated one only when the capacity investment cost is high or the products have low contribution margins.

The PF index is introduced in this paper to measure the benefit of a flexible plant over a group of dedicated plants. The benefits were found to be contingent upon the decision on the required service level.

There is no lack of energy sources in Africa ‐ especially fossil fuels ‐ however many countries in Sub‐Saharan Africa continue to be plagued by energy shortages. This can seriously impede productivity particularly in SME’s and add to energy costs through the need for investment in own generating capacity. In addition the transmission systems are often expensive due to ‘down time’ and this also raises production costs. It also raises costs for households that are effectively forced to generate their own power. This chapter examines three dimensions of the energy ‘gap’ in the context of a number of countries. First, will investment in energy capacity lead to sustainable GDP growth? Second, would investment in renewable or ‘green’ energy capacity make a significant difference? And third, is energy output really such an essential prerequisite for sustainable economic growth?

The purpose of this paper is to present the Business Strategy Assessment Model (BSAM) approach, in an attempt to explore the principle dynamics of an energy‐economic system with emphasis on the private actors' point of view and the impact different policy instruments may have on the decisions of private actors with different characteristics.

In the BSAM model, investment decisions under consideration depend on their perceived financial value. Basis for the financial valuation of an investment option is formed by simulating market dynamics with alternative price scenarios. The outputs include: investment strategies, derived as a function of the (uncertain) state vector of future market conditions and risk premium, calculated by contrasting the expected net present value for the optimal timing of the investment and the expected net present value of an immediate investment generation module of the BSAM, while both of these outputs are estimated for each available technology option.

Different models make different assumptions shedding light upon different aspects of the socio‐economic systems they attempt to analyze and hitherto, no such model succeeds in incorporating all the perceptions that are driving the integration of energy policies. BSAM is based on the notion that a convergence between policy evaluation and business strategy assessment models could be truly beneficial for regulators that aim to derive effective energy policies. Both the algorithm adopted and the structure of the modules of BSAM facilitate the analysis of complex interactions in a firm's decision making process, and even more the what‐if analyses needed for alternative policy measure evaluation.

Setting the framework for a modelling approach that incorporates the role of risk‐return perceptions of private actor's with diverse features, portfolio effects, path dependence and agent competition, into appraising energy and climate policies, and suggest that the heterogeneous world of investors requires reflexive assessment techniques. Above and beyond, understanding the drivers and triggers of firm's investment strategies will allow improving the effectiveness of energy policies.