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To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage is likely to play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. We further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities. Capacity expansion modelling (CEM) approaches need to account for the value of energy storage in energy-system decarbonization. A new Review considers the representation of energy storage in the CEM literature and identifies approaches to overcome the challenges such approaches face when it comes to better informing policy and investment decisions.

This paper addresses a general class of capacity planning problems under uncertainty, which arises, for example, in semiconductor tool purchase planning. Using a scenario tree to model the evolution of the uncertainties, we develop a multistage stochastic integer programming formulation for the problem. In contrast to earlier two-stage approaches, the multistage model allows for revision of the capacity expansion plan as more information regarding the uncertainties is revealed. We provide analytical bounds for the value of multistage stochastic programming (VMS) afforded over the two-stage approach. By exploiting a special substructure inherent in the problem, we develop an efficient approximation scheme for the difficult multistage stochastic integer program and prove that the proposed scheme is asymptotically optimal. Computational experiments with realistic-scale problem instances suggest that the VMS for this class of problems is quite high; moreover, the quality and performance of the approximation scheme is very satisfactory. Fortunately, this is more so for instances for which the VMS is high.

Large and reliable volumes of water are required to cool thermal power plants. Yet across the world growing demands from society, environmental regulation and climate change impacts are reducing the availability of reliable water supplies. This in turn constrains the capacity and locations of thermal power plants that can be developed. The authors present an integrated and spatially explicit energy systems model that explores optimal capacity expansion planning strategies, taking into account electricity and gas transmission infrastructure and cooling water constraints under climate change. In Great Britain, given the current availability of freshwater, it is estimated that around 32 GW of combined cycle gas turbine capacity can be sustainably and reliably supported by freshwater. However, to maintain the same reliability under a medium climate change scenario, this is halved to 16 GW. The authors also reveal that the current benefit of available freshwater to the power sector is ∼£50 billion between 2010 and 2050. Adapting to expected climate change impacts on the reduced reliability of freshwater resources could add an additional £18–19 billion in system costs to the low‐carbon energy transition over the time horizon, as more expensive cooling technologies and locations are required.

The urgency to combat climate change and the widely distributed, increasingly competitive renewable resources in North America are strong arguments to explore scenarios for a renewable energy supply in the region. While the current power system of North America is heavily dependent on fossil fuels, namely natural gas, coal and oil, and some nuclear power plants, some current policies at the state level, and future federal policies are likely to push the share of different renewable sources available in Mexico, the U.S., and Canada. This paper explores three scenarios for a renewable energy supply, using a bottom-up energy system model with a high level of spatial and time granularity. The scenarios span the extremes with respect to connecting infrastructure: while one scenario only looks at state-level supply and demand, without interconnections, the other extreme scenario allows cross-continental network investments. The model results indicate that the North American continent (a) has sufficient renewable potential to satisfy its energy demand with renewables, independent of the underlying grid assumption, (b) solar generation dominates the generation mix as the least-cost option under given renewable resource availability and (c) simultaneous planning of generation and transmission capacity expansion does not result in high grid investments, but the necessary flexibility to integrate intermittent renewable generation is rather provided by the existing grid in combination with short-term and seasonal storages.

In this paper, we consider the optimal management of capacity when a firm faces fixed costs and variable costs to purchase capacity. The firm can also salvage capacity and receive a variable value per unit capacity salvaged, but faces a (different) fixed cost in this case. Each period, the firm faces a stochastic demand, and maintenance costs for capacity that it decides to keep. The firm would thus like to decide how much capacity it should purchase or salvage each period. We introduce a new concept, which we call ( K 1 , K 2 )-concavity, and show that the profit-to-go function satisfies this property. This enables us to characterize the structure of an optimal policy, which is rather complex, consisting of multiple regions in which different decisions are made. We show how special cases of this problem (e.g., no fixed costs, expansion or contraction not allowed) reduce to well-known results, and how ( K 1 , K 2 )-concavity is a generalization of concavity, K -concavity, and sym- K -concavity. We also show how different lead times for purchasing or salvaging capacity can be integrated into the model. Finally, we extend the model to the case where demand is Markov modulated, and a portion of capacity can deteriorate in each period.

PurposeThe rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.Design/methodology/approachThe authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.FindingsThe authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.Originality/valueThe authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

We study strategic capacity planning in the semiconductor industry. Working with a major US semiconductor manufacturer on the configuration of their worldwide production facilities, we identify two unique characteristics of this problem as follows: (1) wafer demands and manufacturing capacity are both main sources of uncertainty, and (2) capacity planning must consider the distinct viewpoints from marketing and manufacturing. We formulate a multi-stage stochastic program with demand and capacity uncertainties. To reconcile the marketing and manufacturing perspectives, we consider a decomposition of the planning problem resembling decentralized decision-making. We develop recourse approximation schemes representing different decentralization schemes, which vary in information requirements and complexity. We show that it is possible to arrive at near optimal solutions (within 6.5%) with information decentralization while using a fraction (16.2%) of the computer time.

Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors’ risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects.

Myanmar remains one of the few exceptions to the rapid diffusion of solar photovoltaics (PV) in power generation mixes. This is surprising considering that Myanmar is one of the countries with the largest technical potential for solar energy among Southeast Asian nations. Solar energy can complement the existing hydropower generation to address endemic energy crisis during the dry season. A continuation of paralysis on investments in solar energy could affect the security and sustainability of the sector in one of the most rapidly growing countries in the region. In this paper, we aimed to identify the barriers preventing solar energy to flourish in Myanmar and to identify policy options to unlock them. We conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis with inputs from relevant stakeholders from the government, private sector, and civil society organizations. Our analysis suggested a consensus on the merits of solar energy among all factors; however, a policy framework to spur investments that contribute both to the national energy system and local development needs to be developed by considering some particularities of Myanmar, such as securing rightful land ownership and limited experience with market mechanisms (e.g., energy auction), for the promotion of investments in the energy sector.

When underground tunnels in coal mines traverse geological structurally abnormal zones (faults, collapse columns, fractured zones, etc.), excavation-induced unloading leads to instability and failure of the engineering rock mass. Rock masses in fractured zones are in elastic, plastic, and post-peak stress states, and the process of excavation through these zones essentially involves unloading under full stress paths. To explore the mechanical response of sandstone under different stress levels, based on the investigation of possible stress paths, a systematic study is conducted on various unloading paths, including elastic-axial compression with unloading of confining pressure, elastic-constant principal stress with unloading of confining pressure, plastic-axial compression with unloading of confining pressure, plastic-constant principal stress with unloading of confining pressure, plastic-constant axial D 1 displacement with unloading of confining pressure, plastic-equal proportional unloading of axial and confining pressures, and post-peak-synchronous unloading of axial and confining pressures. Characteristics of full stress-strain curves under seven unloading paths are obtained. The deformation patterns caused by unloading are analyzed, and the relationship between unloading paths and strain increments is investigated. Results show that, as the degree of unloading increases, the unloading deformation modulus ( E ) in the elastic stress state exhibits a trend of initial increase, then stabilization, followed by decrease, while in the plastic stress state, E gradually decreases. Both elastic and plastic states show an increasing trend in Poisson’s ratio ( µ ). The normalized plastic shear strain γ p /γ p max and dilation angle ( ψ ) conform to a single exponential function, and there is a negative correlation between initial confining pressure and dilation angle. These findings support the enrichment and development of unloading rock mechanics.