Analysis Of Capacity Expansion Research Articles

Game-Theoretic Capacity Expansion Analysis Under Carbon Emission Constraints

In this paper, we develop a long-term capacity expansion analysis model that considers strategic bidding behavior by dominant generation companies together with new entry decisions, including renewables, to meet a long-term carbon reduction constraint. We deploy a centralized quadratic programming formulation of the Nash-Cournot game to analyze day-ahead electricity market prices. In our model, strategic producers simultaneously make investment and production decisions, deploying an open-loop Cournot game. We have implemented the model for Ukraine's day-ahead electricity market, which has been highly prone to market power issues. The case study results show significant inefficiencies in peak prices and explore scenarios of alternative mitigation options and their efficacies. Finally, the case study is extended to also explore implications of a low carbon future through emission-constrained runs, evaluation of the performance-based emission policy, and sensitivity on different portfolios of new renewable investors.

Capacity expansion analysis of UGSs rebuilt from low-permeability fractured gas reservoirs with CO2 as cushion gas

The techniques of pressurized mining and hydraulic fracturing are often used to improve gas well productivity at the later development stage of low-permeability carbonate gas reservoirs, but reservoirs are watered out and a great number of micro fractures are produced. Therefore, one of the key factors for underground gas storages (UGS) rebuilt from low-permeability fractured gas reservoirs with CO2 as the cushion gas is how to expand storage capacity effectively by injecting CO2 to displace water and to develop control strategies for the stable migration of gas–water interface. In this paper, a mathematical model was established to simulate the gas–water flow when CO2 was injected into dual porosity reservoirs to displace water. Then, the gas–water interface migration rules while CO2 was injected in the peripheral gas wells for water displacement were analyzed with one domestic UGS rebuilt from fractured gas reservoirs as the research object. And finally, discussion was made on how CO2 dissolution, bottom hole flowing pressure (BHFP), CO2 injection rate and micro fracture parameters affect the stability of gas–water interface in the process of storage capacity expansion. It is shown that the speed of capacity expansion reaches the maximum value at the fifth cycle and then decreases gradually when UGS capacity is expanded in the pattern of more injection and less withdrawal. Gas–water interface during UGS capacity expansion is made stable due to that the solubility of CO2 in water varies with the reservoir pressure. When the UGS capacity is expanded at constant BHFP and the flow rate, the expansion speed can be increased effectively by increasing the BHFP and the injection flow rate of gas wells in the central areas appropriately. In the reservoir areas with high permeability and fracture-matrix permeability ratio, the injection flow rate should be reduced properly to prevent gas–water interface fingering caused by a high-speed flow. Furthermore, it is necessary to monitor strictly the migration of gas–water interface by using observation wells to prevent gas escape through the edge water or water breakthrough at high-permeability zones. These research results provide a technical and theoretical support for water displacement and capacity expansion of UGS rebuilt from low-permeability fractured gas reservoirs with CO2 as the cushion gas.

An Inexact Dynamic Optimization Model for CO2 Emission Reduction in Subei Region, Northeast China

In this study, an inexact mixed-integer fuzzy robust linear programming model for coupled management of coal and power with consideration of CO2 emissions mitigation system planning (IMIFLP-CCPM) was developed under uncertainty. This model could reach into the closed relationship and interactive characteristics of China’s coal production, electric power generation, and CO2 emissions in coupled coal and power management system and thus explore the applicability of the decarburization facilities and mechanism incorporated in the system through scenario analysis. Based on the integration of interval linear programming, fuzzy robust linear programming, and mixed-integer linear programming, the IMIFLP-CCPM could effectively incorporate and handle uncertainties presented in terms of interval values and fuzzy sets. Also, dynamic analysis of capacity expansion, facility improvement, and inventory planning within a multi-period and multi-option context could be facilitated in this model. The developed IMIFLP-CCPM was applied to a long-term coupled coal and power management with CO2 reduction systems in Subei region, Northeast China. One base scenario and four CO2 reduction scenarios were presented and analyzed to examine the optimal coal-flow allocation patterns and carbon mitigation schemes for the studied system when forced to comply with a given CO2 emission limit. The results indicated that the IMIFLP-CCPM model could provide in-depth analysis of tradeoffs between system costs, energy security, and CO2 emission reduction, thus helping investigate interactive relationships among multiple economical, environmental, and energy structural targets within the study system. Moreover, the attempt of planning coupled coal and power management with CO2 mitigation under uncertainty would provide an effective reference to cope with the dilemma of energy development and CO2 mitigation under the climate change situation in China.

Competition through capacity investment under asymmetric existing capacities and costs

This paper discusses the way that different operational characteristics including existing capacity, scale economies, and production policy have an important influence on the capacity outcomes when firms compete in the market place. We formulate a game-theoretical model where each firm has an existing capacity and faces both fixed and variable costs in purchasing additional capacity. Specifically, the firms simultaneously (or sequentially) make their expansion decisions, and then simultaneously decide their production decisions with these outputs being capacity constrained. We also compare our results with cases where production has to match capacity. By characterizing the firms’ capacity and production choices in equilibrium, our analysis shows that the operational factors play a crucial role in determining what happens. The modeling and analysis in the paper gives insight into the way that the ability to use less production capacity than has been built will undermine the commitment value of existing capacity. If a commitment to full production is not possible, sinking operational costs can enable a firm to keep some preemptive advantage. We also show that the existence of fixed costs can introduce cases where there are either no pure strategy equilibrium or multiple equilibria. The managerial implications of our analysis are noted in the discussion. Our central contribution in this paper is the innovative integration of the strategic analysis of capacity expansion and well-known (s,S) policy in operations and supply chain theory.

Generation Capacity Expansion Analysis: Open Loop Approximation of Closed Loop Equilibria

In this paper we propose a methodology to approximate closed loop capacity equilibria using only open loop capacity equilibrium models. In the closed loop model, generation companies choose capacities that maximize their individual profit in the first stage while the second stage represents the conjectured-price-response market equilibrium. In the open loop model, firms simultaneously choose capacities and quantities to maximize their individual profit, while each firm conjectures a price response to its output decisions. The closed loop equilibrium model is an equilibrium problem with equilibrium constraints, which belongs to a class of problems that is very hard to solve. The open loop equilibrium model is much easier to solve; however, it is also less realistic. With the approximation scheme proposed in this paper, we are able to solve the closed loop model reasonably well when market behavior is closer to oligopoly than to perfect competition by smartly employing open loop models which reduces the computational time by two orders of magnitude. We achieve this by transforming the open loop equilibrium problem into an equivalent convex quadratic optimization problem which can be solved efficiently. Finally, a case study is presented in order to validate the proposed approximation scheme.

Enhancement of economic and ecological sustainability through integrated management of coal and electricity in north China

Abstract System planning of energy resources management is an effective way for supporting socio-economic development and enhancing eco-environmental sustainability. In this study, an inexact coupled coal and power management model with ecological restoration and pollutants mitigation was developed to analyze the impacts of growing coal use and electricity production on eco-environmental quality by integrating a complete set of ecological and environmental constraints. The model can not only effectively handle the uncertainties and complexities of the coupled coal and power management systems, but can also facilitate a dynamic analysis of capacity expansion, facility installation, energy resource inventory, coal blending and environmental regulation changes within a multi-period and multi-option context. The developed model was applied to a long-term coupled coal and power management system planning problem to support the regional eco-environmental sustainability in north China. The interval solutions associated with different risk levels of the constraint violations were obtained, which could be used to formulate decision alternative options. The results generated could also aid decision makers in identifying desirable strategies under various social-economic, environmental and system-reliability constraints with the highest system reliability and the lowest system cost and ecological environment impact. In addition, the tradeoffs between system costs and constraint-violation risks could also be tackled.

An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China

In view of the great contribution of coal-fired units to CO2 emissions, the coupled coal and power system with consideration of CO2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO2 mitigation strategy associated with stochastic power-generation demand under specific CO2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO2 emission mitigation, as well as facility capacity upgrade and expansion under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO2 emission reduction could be analyzed.

Implications of capacity expansion under uncertainty in oil industry

A multi-period stochastic planning model with finite number of realizations has been developed and implemented for a supply and distribution network of a petroleum organization operating under uncertain market conditions. In this paper, we present the analysis of capacity expansion under uncertainty from the viewpoints of the decision making process on the optimal allocation of investment. The optimization model is developed using real data from oil industry and described an actual petroleum products distribution system as a case study. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in planning and policy analysis.

IFTCIP: An Integrated Optimization Model for Environmental Management under Uncertainty

In this study, an interval-fuzzy two-stage chance-constrained integer programming (IFTCIP) method is developed for supporting environmental management under uncertainty. The IFTCIP improves upon the existing interval, fuzzy, and two-stage programming approaches by allowing uncertainties expressed as probability distributions, fuzzy sets, and discrete intervals to be directly incorporated within a general mixed integer linear programming framework. It has advantages in uncertainty reflection, policy investigation, risk assessment, and capacity-expansion analysis in comparison to the other optimization methods. Moreover, it can help examine the risk of violating system constraints and the associated consequences. The developed method is applied to the planning for facility expansion and waste-flow allocation within a municipal solid waste management system. Violations of capacity constraints are allowed under a range of significance levels, which reflects tradeoffs between the system cost and the constraint-violation risk. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different risk levels. They are useful for generating desired decision alternatives with minimized system cost and constraint-violation risk under various environmental, economic, and system-reliability conditions. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost.

Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan

In this paper, we present the near-term analysis of capacity expansion under various uncertainties from the viewpoints of the decision-making process on the optimal allocation of investment and the value of information. An optimization model based on two-stage stochastic programming was developed using real data to describe the Japanese energy system as a case study. Different uncertainty parameters were taken into consideration by a disaggregate analysis of a bottom-up energy modeling approach, including end-use energy demands, plant operating availability and carbon tax rate. Four policy regimes represented as energy planning or policy options were also studied, covering business as usual, renewable energy target, carbon taxation and nuclear phase-out regimes. In addition, we investigated the role of various energy technologies and the behavior of the value of information with respect to the probability function of the worst-case scenario. This value of information provides decision makers with a quantitative analysis for the cost to obtain perfect information about the future. The developed model could be regarded as an applicable tool for decision support to provide a better understanding in energy planning and policy analyses.

Optimizing the integration of photovoltaic systems with electric utilities

The author introduces a comprehensive simulation package whereby the optimal size, operation, performance and economics of a photovoltaic (PV) system can be determined in the utility-integrated mode. The central issue is to present a single definitive optimization model. A stepwise analytical methodology starting at the solar resource and culminating in the value of the PV system in terms of avoided costs is provided. The methodology includes processing of the solar irradiance; identification of the PV system's configuration and operational features; identification of real-time system controls in the presence of PV generation; security assessment in the presence of PV and production costing and capacity expansion analysis with PV. The optimization package is subdivided into five different subgroups based on their respective purposes in the context of the overall scheme. The author describes the functions of each subgroup.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Capacity expansion—analysis of simple models with applications, by John Freidenfels, North Holland, 1981, 291 pp. Price: $39.50

Capacity expansion—analysis of simple models with applications, by John Freidenfels, North Holland, 1981, 291 pp. Price: $39.50