Capacity Expansion Costs Research Articles

The impact of national policies on Europe-wide power system transition towards net-zero 2050

This research aims to investigate the potential impact of national policies on the attainment of Europe's goal of achieving net-zero greenhouse gas emissions by 2050. Specifically, it analyses the effects of policies on the power sector, by evaluating capacity expansion portfolios, import reliance, and costs by 2050. A linear programming model, the IESA-EUPS, is utilized to optimize the expansion and operation of the power system, considering 28 nodes and hourly temporal resolution. The study includes five scenarios from 2020 to 2050, with varying levels of biomass and nuclear penetration based on existing member state policies. Results show that by 2050, changes mainly occur in the interplay between firm capacities and cross-border transmission levels. Limiting biomass can significantly increase nuclear energy generation, while enforcing all policies leads to a 40 % rise in cross-border transmission by 2050, due to imbalances between countries. Some member states, such as Spain and Finland, are less affected, whereas others are heavily reliant on firm nuclear capacities. Western European countries with strict biomass and nuclear restrictions may see a boost in nuclear installations in countries allowing it. Member states without both nuclear and biomass may rely more on variable renewables, resulting in surplus electricity and increased LCOE.

Renewable electricity capacity planning with uncertainty at multiple scales

We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO2 constraint that limit respectively non-renewable capacity, non-renewable generation, and CO2 emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand’s South Island.

Multi-Objective Reinforcement Learning Based Healthcare Expansion Planning Considering Pandemic Events.

Hospital capacity expansion planning is critical for a healthcare authority, especially in regions with a growing diverse population. Policymaking to this end often requires satisfying two conflicting objectives, minimizing capacity expansion cost and minimizing the number of denial of service (DoS) for patients seeking hospital admission. The uncertainty in hospital demand, especially considering a pandemic event, makes expansion planning even more challenging. This work presents a multi-objective reinforcement learning (MORL) based solution for healthcare expansion planning to optimize expansion cost and DoS simultaneously for pandemic and non-pandemic scenarios. Importantly, our model provides a simple and intuitive way to set the balance between these two objectives by only determining their priority percentages, making it suitable across policymakers with different capabilities, preferences, and needs. Specifically, we propose a multi-objective adaptation of the popular Advantage Actor-Critic (A2C) algorithm to avoid forced conversion of DoS discomfort cost to a monetary cost. Our case study for the state of Florida illustrates the success of our MORL based approach compared to the existing benchmark policies, including a state-of-the-art deep RL policy that converts DoS to economic cost to optimize a single objective.

A multi-objective stochastic programming approach with untrusted suppliers for green supply chain design by uncertain demand, shortage, and transportation costs

In recent decades, people are paying more attention to protecting the environment and biological resources. One of the most important issues of environmental pollution is air pollution, with production and transportation contributing a significant number of emissions. Considering the various warnings about the excessive increase in the amount of carbon dioxide on the planet, it is necessary to investigate the solutions to reduce the emission of carbon dioxide. On the other hand, in the production cycle, providing more comprehensive models and finding more optimal designs will play a significant role in reducing energy consumption and increasing profitability. Models with the most uncertain parameters are more practical and closer to the real world. In this paper, we consider a multi-objective stochastic programming approach for green supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage, and capacity expansion costs are all considered uncertain parameters. At the same time, environmental approaches to reduce air pollution, (specifically reducing carbon dioxide emissions), are presented. Our multi-objective model includes the minimization of the sum of the total cost, the minimization of the variance of the total cost, the minimization of the financial risk or the probability of not meeting a certain budget, and the minimization of the amount of pollution consequent of production and transportation machines. In the following case study, a three-tier supply chain with four suppliers, four production centers, and three product distribution centers with uncertain demand, suppliers, processing, transportation, cost shortages, and capacity development are examined. To solve the model, two meta-heuristic algorithms (multi-objective genetics with faulty sorting and particle swarming) have been developed. The computational results and optimal designs of the supply set were obtained after the implementation of the algorithms, and finally, by performing Sensitivity Analysis and statistical tests, showed that there are two algorithms with good performance and in most cases, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is superior to the Multi-Objective Particle Swarm Optimization (MOPSO).

Analysis of multi-output hybrid energy systems interacting with the grid: Application of improved price-taker and price-maker approaches to nuclear-hydrogen systems

The growing recognition of the value of hydrogen as an energy intermediate in supporting future power systems with high shares of variable renewable energy has prompted many studies to quantify the economic potential of multi-output hybrid systems, which are one type of integrated energy systems (IES). Because of the complexity of modeling multiple sectors, these studies typically use simplified modeling approaches to capture the interactions between sectors. In this study, we explore the implications of alternative modeling approaches for nuclear-hydrogen IES focusing on a power system in the Midwest United States. We combine highly resolved capacity expansion and production cost modeling tools of the power system with a detailed hydrogen system optimization tool to determine the optimal electrolyzer and storage sizing and optimal operations of the nuclear-hydrogen hybrid resource across three future study years. We compare economic and operational outcomes across a spectrum of modeling approaches, including a non-hybridized base approach; a traditional price-taker approach that does not include the impact of hydrogen production on the electricity system; a power-system-focused price-maker approach that does not account for temporal hydrogen constraints; and two improved price-taker and price-maker approaches that each address the impact of revenue-optimal levels of electricity production on the resulting power system and temporal hydrogen constraints on the overall feasible solution. Results show how a traditional price-taker approach can overestimate the economic benefits of multi-output nuclear-hydrogen IES compared to our two improved approaches that estimate both hydrogen system constraints and power system interaction. We find that hydrogen output requirements and storage size limits are key drivers to overall operations and some economic outcomes. Under our assumed constant hydrogen output requirement, storage costs, test system, and modeling approaches, our results indicate that hybridization can provide a net benefit, but results are sensitive to the treatment of hydrogen revenues and electricity prices as impacted by the power system evolution.

A multi-model framework for assessing long- and short-term climate influences on the electric grid

Climate change influences many aspects of the electric grid, but prior work and industry practices often ignore the potential effects of changing climate, or they only consider a single effect or individual effects in isolation. Challenges vary with each grid and include adapting to long-term trends such as changing temperature and precipitation or shorter-term events such as drought or storms that could increase in frequency or intensity. Here we present a multi-model framework designed to analyze the effects of long and short-term climate impacts in combination. This framework couples capacity expansion and production cost models with hydrologic models and future climate scenario data to analyze alternative climate and energy futures at high spatial, temporal, and process resolutions. We constructed and evaluated the results of a suite of simulated scenarios exploring climate impacts on capacity investment and stress-tested the resulting future infrastructures using hourly dispatch modeling under alternative drought and load conditions. We demonstrate the approach through a case study of the U.S. Western Interconnection, where climate impacts depend on interactions between temperature-induced load, water availability for hydropower, technology competitiveness, and demand flexibility. Changes in 2038 generating capacity range from −8.5–16.6 GW, and changes in 2038 transmission capacity range from −1–2 GW. Capacity increases are driven by higher load from higher temperatures, while capacity reductions can be achieved in scenarios with higher future hydropower availability and increased demand flexibility. Scenarios requiring additional capacity cost an additional $5–$17 billion (discounted) from 2018 to 2038; however, scenarios with capacity reductions cost $1–$18 billion less. Stress tests on four 2038 infrastructures demonstrated that the identified systems were able to serve at least 99.999% of load and 99.96% of reserves. However, drought and unexpected high-load conditions can result in reduced capacity to respond to contingency events we did not model. Although these results are system and scenario specific, they highlight the importance of considering multiple climate change impacts simultaneously in long-term planning efforts and demonstrate a multi-model, multiscale approach that can be flexibly applied to any system and set of climate change concerns.

Minimum Cost of Capacity Expansion for Time-Limited Transportation Problem On-Demand

The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all origins’ supply and all destinations’ demands as well as the expanding cost is minimum. Actually, MCCETLTPD is a balance transportation problem and a variant problem of minimum cost maximum flow problem. In this paper, by creating a mathematical model and constructing a network with lower and upper arc capacities, MCCETLTPD is transformed into searching feasible flow in the constructed network, and consequently, an algorithm MCCETLTPD-A is developed as MCCETLTPD’s solution method basing minimum cost maximum flow algorithm. Computational study validates that the MCCETLTPD-A algorithm is an efficient approach to solving the MCCETLTPD.

Strategic and economic implications of forward contracting in the presence of capacity expansion

This paper studies the strategic and economic implications of forward contracting in the context of electricity markets with capacity expansion. Generation firms can sign forward contracts with consumers long before competing in the spot market. Holding these contracts, the firms, short of capacity, start their capacity expansion and finish it prior to spot trading. Although forward contracting is always regarded as a critical tool to enhance firm competition and promote social welfare except for risk-hedging, it is still unclear how capacity expansion may influence the effectiveness of forward contracting since capacity expansion strategically changes the firms’ competition strategies and thus market performance. Therefore, we build a stylized game model to explore the role of capacity expansion in shaping the effectiveness of forward contracting theoretically. The model includes forward contracting, capacity expansion and spot trading sequentially, which is solved by backward induction. Comparing the equilibrium outcomes of this model to the case of no forward contracting, we derive several important findings. First, we show that forward contracting occurs if and only if the marginal capacity expansion cost is beneath a threshold level. Second, when forward contracting happens, each firm’s forward sales are equal to its expected spot sales and forward contracting intensifies competition between firms as well as improves social welfare. Third, forward contracting doesnot affect the size of potential excess expansion. These findings illustrate the importance of capacity expansion costs and help us understand how forward contracting works.

Prosumage of solar electricity: Tariff design, capacity investments, and power sector effects

We analyze how tariff design incentivizes households to invest in residential photovoltaic and battery storage systems, and explore selected electricity sector effects. To this end, we develop an open-source electricity sector model that explicitly features prosumage agents and apply it to German 2030 scenarios. Results show that lower feed-in tariffs substantially reduce investments in residential photovoltaics, yet optimal battery sizing and self-generation are relatively robust. With increasing fixed parts of retail tariffs and, accordingly, lower volumetric retail rates for grid consumption, households have lower incentives for self-consumption. As a consequence, optimal battery capacities and self-generation are smaller, and households contribute more to non-energy power sector costs. A cap on hourly feed-in by households may relieve distribution grid stress without compromising PV expansion or prosumage models for households. When choosing tariff designs, policy makers should not aim to (dis-)incentivize prosumage as such, but balance effects on renewable capacity expansion and system cost contribution.

Flexible CCS Technologies To Reduce the Cost of Net-Zero Carbon Electricity Systems

Studies have shown that firm, low-carbon sources of power such as fossil-fueled power plants with carbon capture and storage (CCS) can reduce the cost of net-zero carbon electricity systems. But other developments on the path to decarbonization, particularly the proliferation of variable renewable energy (VRE) such as wind and solar, complicate the design of CCS-equipped plants, which must operate with reduced capacity factors and increased cycling. An increasing number of jurisdictions are committing to net-zero carbon electricity systems with high penetration of VRE. This paper considers the implications for CCS- equipped power plants via examination of grid capacity expansion and production cost modelling. The need to reconsider the optimal design and operations of CCS plants is presented, with focus on tradeoffs between capital cost, efficiency, and CO2 capture rate. Approaches to economic optimization via minimizing cost and maximizing operational profits are discussed. Finally, the case will be made that future CCS plants must either enable maximum power plant flexibility or change the operating paradigm so that plants can operate under more steady-state conditions and shift their electrical output to the grid via storage or provide another service such as removing CO2 from the air.

Investing in suppliers with capacity constraints in a decentralized assembly system

This study investigates a decentralized assembly system, in which a single downstream assembler buys complementary components from multiple upstream suppliers, and then assembles them into a final product to satisfy an uncertain market demand. Suppliers have capacity constraints, and the assembler considers to invest in suppliers to increase their capacities. The investment problem of the assembler is a complex multi-dimensional optimization problem. We first characterize the optimal pricing and production decisions of all supply chain members assuming that the investment strategy is given. We then derive optimal investment strategy for the assembler. We show that the assembler's optimal total investment increases in the retail price, and decreases in both the component production cost and the assembly cost, but is nonmonotonically related to the component capacity expansion cost. We also show that the performance of the entire supply chain highly depends on the allocation of the total component cost between the capacity expansion and production activities.

Modelling geothermal resource utilization by incorporating resource dynamics, capacity expansion, and development costs

If geothermal resources are utilized excessively for electricity production, the reservoir can be temporarily (almost) depleted. Regeneration of an overutilized resource can take a long time. This paper presents a System Dynamics model for geothermal power plant expansion considering the dynamics of geothermal resources on a system’s level. The model consists of three main modules: resource dynamics, plant construction, and geothermal economics. Thereby, it captures the following dynamics: The geothermal field stock decreases due to utilization for electricity production and increases through natural recharging. Changes in geothermal stock, and thus in well production capacity, lead to additional well requirements to maintain electricity production levels. This influences the unit cost of electricity. To show the effect of geothermal resource dynamics on a national system’s level the model is applied to Iceland’s geothermal resources. Four main scenarios are simulated and compared based on the level of resource utilization, assuming high and low demand growth (i.e. 2% and 4.4%), and whether geothermal resource dynamics are incorporated or not. Sensitivity analysis is performed with respect to well capital cost and natural recharging rates for geothermal fields. The findings indicate that geothermal resource dynamics significantly increase costs because of the well drilling activities that are required to maintain production.

Supply Chain Coordination in the Context of Green Marketing Efforts and Capacity Expansion

This paper focuses on coordination issues related to the green supply chain with capacity constraints and green marketing efforts. We build a two-stage green supply chain, in which the upstream manufacturer has a certain amount of installed capacity to produce green product, yet can expand its capacity through a Cloud Manufacturing (CM) Platform once its existing capacity becomes insufficient, while the downstream retailer expends green marketing effort to promote the green product. In particular, we analyze the interaction between the capacity expansion options of the manufacturer and the green marketing efforts of the retailer. Aiming to mitigate the inefficiency under a decentralized green supply chain, we design a contract that combines cost-sharing and revenue-sharing in green marketing in order to coordinate the supply chain. The results show that: (1) when the manufacturer’s existing capacity falls below a certain threshold, it will choose to expand its capacity. The threshold is related to existing capacity, capacity expansion cost coefficient, green marketing cost coefficient, and sensitivity coefficient of demand to green marketing. (2) Under low capacity, if the capacity expansion cost coefficient is large, a higher consumer environmental awareness or preference for green products will weaken the retailer’s motivation for expending green effort. (3) A contract for cost-sharing and revenue-sharing in green marketing can fully coordinate the green supply chain, whereby the two share proportions are equal and meet certain constraints.

Bi-objective optimization model with societal constraints for green closed loop supply chain network

Scrap-battery collection, recovery and new-battery production activities in the battery industries have increased a large quantity of materials and products distribution between the facilities. This situation leads to the sustainability issue in terms of higher transportation cost, greenhouse gas (GHG) emission and drivers fatigue (long driving). In order to address the sustainable issues, the present closed loop supply chain network (CLSCN) is customised. The objective of this paper is to develop bi-objective multi product multi echelon multi period green CLSCN model with societal constraints (GCLSCN-SC) to minimise: 1) the sum of distribution centres' (DC) storage capacity expansion cost and transportation cost; 2) the GHG emission (CO2 and NOx). The storage capacity expansion strategy and the transportation strategy are established in the proposed model to stimulate the bi-directional flow of the products and materials between facilities and the model is solved by using GAMS-23.5. The performance of proposed model has been compared with the present model and the evaluation demonstrates that the proposed model is efficient. The analysed results of the proposed model under different scenarios are presented in this paper.

Bi-objective optimisation model with societal constraints for green closed loop supply chain network - a case of battery industry

Scrap-battery collection, recovery and new-battery production activities in the battery industries have increased a large quantity of materials and products distribution between the facilities. This situation leads to the sustainability issue in terms of higher transportation cost, greenhouse gas (GHG) emission and drivers fatigue (long driving). In order to address the sustainable issues, the present closed loop supply chain network (CLSCN) is customised. The objective of this paper is to develop bi-objective multi product multi echelon multi period green CLSCN model with societal constraints (GCLSCN-SC) to minimise: 1) the sum of distribution centres' (DC) storage capacity expansion cost and transportation cost; 2) the GHG emission (CO2 and NOx). The storage capacity expansion strategy and the transportation strategy are established in the proposed model to stimulate the bi-directional flow of the products and materials between facilities and the model is solved by using GAMS-23.5. The performance of proposed model has been compared with the present model and the evaluation demonstrates that the proposed model is efficient. The analysed results of the proposed model under different scenarios are presented in this paper.

Optimisation of online retailer pricing and carrier capacity expansion during low-price promotions with coordination of a decentralised supply chain

In recent years, many online retailers in China set low prices on 11 November, which stimulates huge delivery demand and results in many problems although carriers make an effort to increase their delivery capacities temporarily. To circumvent this difficulty, we consider a supply chain consisting of an online retailer, who can set price to influence the demand, and a capacitated carrier, whose capacity can be expanded at a high cost. We derive the optimal decisions in the centralised and decentralised decision systems, and compare the performances of the two systems. We find that the optimal decisions, and which system has lower price, larger capacity increment, and more late delivered goods depends on the model parameters (the market scale, the late delivery costs, the capacity expansion cost, the delivery fee, and the demand uncertainty). Specially, we show that, contrary to the traditional channel, the online retailer in the decentralised system may set lower price and the carrier has less incentive to expand capacity in the decentralised system in some situations, which underlines the need for coordination. In addition, we propose coordination contracts to improve the overall performance of the supply chain under deterministic and random demands.

Decision rule-based method for flexible multi-facility capacity expansion problem

ABSTRACTStrategic capacity planning for multiple-facility systems with flexible designs is an important topic in the area of capacity expansion problems with random demands. The difficulties of this problem lie in the multidimensional nature of its random variables and action space. For a single-facility problem, the decision rule method has been shown to be efficient in deriving desirable solutions, but for a Multiple-facility Capacity Expansion Problem (MCEP), it has not been well studied. This article designs a novel decision rule–based method for the solution of an MCEP with multiple options, discrete capacity, and a concave capacity expansion cost. An if–then decision rule is designed and the original multi-stage problem is thus transformed into a master problem and a multi-period sub-problem. As the sub-problem contains non-binding constraints, we combine a stochastic approximation algorithm with a branch-and-cut technique so that the sub-problem can be further decomposed across scenarios and be solved efficiently. The proposed decision rule–based method is also extended to solving the MCEP with fixed costs. Numerical studies in this article illustrate that the proposed method affords not only improved performance relative to an inflexible design taken as benchmark but also time savings relative to approximate dynamic programming analysis.

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.

CO2 volatility impact on energy portfolio choice: A fully stochastic LCOE theory analysis

Market based pricing of CO2 was designed to control CO2 emissions by means of the price level, since high CO2 price levels discourage emissions. In this paper, it will be shown that the level of uncertainty on CO2 market prices, i.e. the volatility of CO2 prices itself, has a strong influence not only on generation portfolio risk management but also on CO2 emissions abatement. A reduction of emissions can be obtained when rational power generation capacity investors decide that the capacity expansion cost risk induced jointly by CO2 volatility and fossil fuels prices volatility can be efficiently hedged adding to otherwise fossil fuel portfolios some nuclear power as a carbon free asset. This intriguing effect will be discussed using a recently introduced economic analysis tool, called stochastic LCOE theory. The stochastic LCOE theory used here was designed to investigate diversification effects on energy portfolios. In previous papers this theory was used to study diversification effects on portfolios composed of carbon risky fossil technologies and a carbon risk-free nuclear technology in a risk-reward trade-off frame. In this paper the stochastic LCOE theory will be extended to include uncertainty about nuclear power plant construction times, i.e. considering nuclear risky as well, this being the main uncertainty source of financial risk in nuclear technology. Two measures of risk will be used, standard deviation and CVaR deviation, to derive efficient frontiers for generation portfolios. Frontier portfolios will be analyzed in their implications on emissions control.

An interactive optimization model for energy systems planning associated with clean-energy development under uncertainty

Summary In this study, an interactive fuzzy chance-constrained resolution (IFCR) method is developed for supporting energy systems planning (ESP) under uncertainty. IFCR can not only tackle multiple uncertainties presented as fuzzy membership functions and probability distributions using an interactive resolution method, but also enable decision makers to seek optimal solutions between satisfaction degree of objective and feasibility of constraints. Then, an IFCR-ESP model is formulated for energy systems of Bayingolin Mongol Autonomous Prefecture (abbreviated as Bazhou). Results provide decision makers with a complete view of the relationship between uncertain inputs and solutions. Policy-based solutions concerning energy consumption, electricity generation, capacity expansion, pollutant emission, and system cost are analyzed, which can help decision makers to identify desired strategies for ESP. Results indicate that transitioning from the conventional energy system to a sustainable one is associated to policy support, resources availability, energy supply security, and capital investment; clean energy policy (e.g. shift from coal to gas) is an effective way to facilitate the local energy system development in a sustainable manner; the solutions under different feasibility degrees can help decision makers to conduct in-depth analyses of tradeoffs between system cost and constraint-violation risk in an interactive way. Copyright © 2016 John Wiley & Sons, Ltd.