Capacity Expansion Planning Research Articles

Early transformation of the Chinese power sector to avoid additional coal lock-in

Emission reduction from the coal-dominated power sector is vital for achieving China’s carbon mitigation targets. Although the coal expansion has been slowed down due to the cancellation of and delay in new construction, coal-based power was responsible for over one third of China’s energy-related CO2 emissions by 2018. Moreover, with a technical lifetime of over 30 years, current investment in coal-based power could hinder CO2 mitigation until 2050. Therefore, it is important to examine whether the current coal-based power planning aligns with the long-term climate targets. This paper introduces China’s Nationally Determined Contribution (NDC) goals and an ambitious carbon budget along with global pathways well-below 2 degrees that are divided into five integrated assessment models, which are two national and three global models. We compare the models’ results with bottom-up data on current capacity additions and expansion plans to examine if the NDC targets are in line with 2-degree pathways. The key findings are: 1. NDC goals alone are unlikely to lead to significant reductions in coal-based power generation. On the contrary, more plants may be built before 2030; 2. this would require an average of 187–261 TWh of annual coal-based power capacity reduction between 2030 and 2050 to achieve a 2 °C compatible trajectory, which would lead to the stranding of large-scale coal-based power plants; 3. if the reduction in coal power can be brought forward to 2020, the average annual coal-based power reduction required would be 104–155 TWh from 2020 to 2050 and the emissions could peak earlier; 4. early regulations in coal-based power would require accelerated promotion of alternatives between 2020 and 2030, with nuclear, wind and solar power expected to be the most promising alternatives. By presenting the stranding risk and viability of alternatives, we suggest that both the government and enterprises should remain cautious about making new investment in coal-based power sector.

Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models

Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

Capacity Expansion of Electricity Sector Using Multiple Sustainability Indicators

The fast-paced growth in electricity demand across the globe raises the question of availability and sustainability of the electricity sector. This paper considers five different sustainability indicators: the risk to human lives, carbon footprint, water footprint, land footprint, and energy return on energy investment (EROI), for planning the capacity expansion of different power plants to fulfil the electricity demand sustainably and cost-effectively. A method is developed for the capacity expansion planning of power sector using Pinch Analysis approach. Different sustainability indicators are combined into an aggregated sustainability indicator by using different methods: p-norms (grid norm, Euclidean norm, and infinity norm) and Analytic Hierarchy Process (AHP). The prioritisation for commissioning of new power plants, to assure the future electricity demand, is accomplished using the concepts of prioritised cost. It is shown in this paper that the aggregate sustainability indicator and the prioritising sequence of different power plants help in achieving sustainable electricity demand in a cost-effective (both capital cost and overall annualised cost) way. Physical insights of Pinch Analysis help in explaining the optimisation results and optimal energy mix. The proposed method, developed for capacity expansion, is demonstrated in this paper through a case study of the Indian electricity sector.

Multistage adaptive optimization using hybrid scenario and decision rule formulation

AbstractScenario‐based stochastic programming and linear decision rule (LDR)‐based robust optimization are prevalent methods for solving multistage adaptive optimization (MSAP) problems. In practical applications such as capacity expansion planning of chemical processes, often multiple sources of uncertainty affect the problem which introduces challenges to traditional stochastic optimization methods. While a large number of uncertain parameters exist in the problem, using scenario‐based method results in very large problem size and the solution becomes computationally expensive. In addition, when the constraints include multiplication of uncertain parameters and adaptive variables, the constraints are not linear with respect to uncertain parameters when the LDR method is used. In order to address these challenges, we propose two different hybrid methods where scenario and decision rule methods are combined to solve the MSAP problem. The article demonstrates the computational performance of the proposed hybrid methods using two chemical process planning examples.

Integrated Planning for Distribution Automation and Network Capacity Expansion

Due to the large amount of resources needed for implementing distribution automation systems (DASs), distribution companies (DISCOs) should develop long term plans for this project. Similarly, DISCOs have network capacity expansion (NCX) plans which specify the scheme of constructing the feeders and substations. Since considering DAS may lead to more efficient NCX plans, the planner should revise the NCX plan through the DAS planning. On the other hand, for DAS planning it is necessary to consider a NCX plan as a platform to determine the time and location for installation of automation equipment. Therefore the planner should simultaneously work on two plans, each of them affects the other. To facilitate this complicated planning procedure, this paper presents a novel method for integrated planning for NCX and DAS implementation. The proposed method develops a comprehensive plan in which the optimal scheme and schedule for constructing the lines and substations and installation of automation equipment is specified. To reduce the huge computational burden, an optimization method based on genetic algorithm is proposed. The proposed method is evaluated on an illustrative test network and the obtained results are presented and discussed.

Long-term electricity sector expansion planning: A unique opportunity for a least cost energy transition in South Africa

With climate change being near unequivocally linked to anthropogenic greenhouse gas (GHG) emissions there is an ongoing move to decarbonise economies globally with the electricity sector identified as a primary focus for most countries’ strategies. This research presents a Business-as-Usual scenario and electricity sector capacity expansion plans to determine a least-cost as well as decarbonised electricity mix for South Africa. A significant finding is that South Africa has the unique opportunity to transition from an existing carbon and water intensive coal-based electricity system to a low carbon and water intense electricity system at least-cost. The approach applies a generation capacity expansion optimisation using mixed-integer linear programming (MILP) to co-optimise energy and ancillary services. The research finds that it is least cost for any new generation capacity investment to be solar photovoltaics (PV), wind and flexible supply-side or demand-side capacity with a >75% renewable energy (RE) share by 2050, replacing existing capacity as it is decommissioned and meeting new demand. By 2050, the Least-cost scenario is conservatively $ 5.1-bln/year cheaper than Business-as-Usual (≈12%) and $ 7.8-bln/year cheaper (≈20%) when applying expected cost assumptions. A Decarbonise scenario using conservative cost assumptions has a >90% RE share by 2050. It is $ 4.8-bln/year more expensive when compared to Least-Cost where a ≈60% decarbonised electricity system is possible by 2050. Business-as-Usual as well as Decarbonise scenarios have similar costs when applying conservative cost assumptions while the Decarbonise scenario becomes $ 4.8-bln/year cheaper than Business-as-Usual (≈11%) when applying expected cost assumptions. This is while being 95% decarbonised while Business-as-Usual is 20% decarbonised by 2050.

Optimal Operational Strategy for Power Producers in Korea Considering Renewable Portfolio Standards and Emissions Trading Schemes

Globally, many countries are experiencing economic growth while concurrently increasing their energy consumption. Several have begun to consider a low-carbon energy mix to mitigate the environmental impacts caused by increased fossil fuel consumption. In terms of maximizing profits, however, power producers are not sufficiently motivated to expand capacity due to high costs. Thus, the Korean government initiated the Renewable Portfolio Standard (RPS), an obligation to generate a certain proportion of a producer’s total generation using renewable energy for power producers with capacities of 500 MW or more, and the Emissions Trading Scheme (ETS), designed to attain a carbon emissions reduction goal. We propose a mathematical model to derive the optimal operational strategy for maximizing power producer profits with a capacity expansion plan that meets both regulations. As such, the main purpose of this study was to obtain the optimal operational strategy for each obligatory power producer. To that end, we defined a 2 × 2 matrix to classify their types and to conduct scenario-based analyses to assess the impact of major factor changes on solutions for each type of power producer. Finally, for the power generation industry to operate in a sustainable and eco-friendly manner, we extracted policy implications that the Korean government could consider for each type of power producer.

Methodical approach to assessing the optimal development scale of distributed cogeneration in the UPS of Russia for the long-term

The original methodological approach to correct assessment of the prospects and the development scale of distributed cogeneration in the long-term forecasts of the electric power industry is proposed in the article. Required suggestions for modification of the modeling tools used for the LCOE-based screening analysis and capacity expansion planning are presented. The results of application of the proposed methodological approach, which show the significant potential for distributed cogeneration development in the UPS of Russia for different tariff groups of consumers, are given.

Interdependent Critical Infrastructure Model (ICIM): An agent-based model of power and water infrastructure

The comfort, mobility, and economic well-being of a population depends on reliable and affordable electric power services, which in turn requires a sustainable water supply. It is therefore increasingly important to analyze the sustainability and resilience of mid- and long-term electric utility and water system capacity expansion plans. Due to the inherent interdependency between power and water critical infrastructure, these expansion plans should be analyzed with respect to potential challenges posed by climate change and other risks. Decision-makers therefore require tools that facilitate an integrated analysis that captures the interdependency of power and water to better inform future expansion plans. Here we develop an agent-based model of a typical regional power system that incorporates the features of specific plant types and their cooling systems that are dependent on adequate water supplies at appropriate temperatures to support full power operation. The effects of capacity expansion plans, power demand growth, climate change, and extreme events are analyzed through different scenarios designed to illustrate the utility of such a model and show where it can aid in mid- and long-term planning.

Analysis of electricity investment strategy for Bosnia and Herzegovina

This Case Study considers generation capacity expansion planning in Bosnia and Herzegovina (BiH) including a range of issues related to renewable, energy efficiency, local emission and carbon reduction commitment policies. In addition, our analysis considers an outlook of wholesale electricity prices in the region. We have also compared our findings with the country's current official plan (“Indicative Plan”), which includes development of as many as four additional lignite-based plants. Our findings suggest that BiH, as a low-cost producer in the region, has significant opportunities to modernize and expand its current generation system with nearly €3 billion in new investments. This investment level is much lower than the €5 billion investment program promulgated in the Indicative Plan. These differences primarily have to do with the treatment of lignite plants. Since the end of the war roughly twenty years ago, the country has progressed quickly. Aspirations to join the EU compose a significant part of the authorizing environment for the power sector.

Power system decarbonisation with Global Energy Interconnection – a case study on the economic viability of international transmission network in Australasia

The electric energy system in Indonesia is undergoing with the challenges of fast-increasing electricity demand, carbon constraints, and rising costs. Using our model of the Australian and Indonesian electrical grids (either separately or interconnected) that incorporates operational flexibility in capacity expansion planning, we first show that meeting the projected demand for Java and Bali—the main Indonesian grid, with 100% locally integrated renewables by 2050 would be challenging. However, a submarine high-voltage DC (HVDC) link connecting Indonesia’s Java-Bali power grid to the Australian National Electricity Market (NEM) grid through the Northern Territory would help alleviate this situation, given Australia’s abundant renewable energy resources. Then, our model reveals that the Australian NEM could also profit from additional renewables if connected to the Northern Territory through a ground HVDC transmission line to gather intermittent wind and solar generation, which would be curtailed otherwise if unused by Indonesia through the submarine link. Despite the expensiveness of long HVDC links, the wholesale electricity cost of the integrated 100% renewable Australasia power system could be reduced by over 16%, from $AUD177/MWh with only local renewables to $AUD148/MWh with integrated HVDC transmission. The model retrieved the optimal international HVDC link with capacity of 43.8 GW, and the optimal regional HVDC transmission line with a capacity of 5.5 GW. To the best of our knowledge, this is the first detailed model on power system decarbonisation planning for both Australian NEM and Indonesian Java-Bali power grid considering HVDC interconnections.

Geospatial multi-criteria analysis for identifying high priority clean energy investment opportunities: A case study on land-use conflict in Bangladesh

Bangladesh is a globally important emerging economy with rapidly increasing energy demand. The Bangladeshi government's primary capacity expansion plan is to install 13.3 GW of new coal by 2021, including the 1.3 GW Rampal coal power plant to be developed in the Sundarbans. Inadequate geospatial and economic information on clean energy investment opportunities are often a significant barrier for policy makers. Our study helps fill this gap by applying a new method to assess energy investment opportunities, with focus on understanding land-use conflicts, particularly important in this context as Bangladesh is constrained on land for agriculture, human settlements, and ecological preservation. By extending a geospatial multi-criteria analysis model (MapRE) we analyze the cost of various renewable energy generation technologies based on resource availability and key siting criteria such as proximity to transmission and exclusion from steep slopes, dense settlements or ecologically sensitive areas. We find there is more utility-scale solar potential than previously estimated, which can be developed at lower costs than coal power and with minimal cropland tradeoff. We also find significant potential for decentralized roof-top solar in commercial and residential areas. Even with a conservative land use program that reserves maximum land for agriculture and human settlement, there is more renewable energy capacity than needed to support Bangladeshi growth. This study provides critical and timely information for capacity expansion planning in South Asia and demonstrates the use of geospatial models to support decision-making in data-limited contexts.

Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning

The planning of an energy system with high penetration of renewables is becoming increasingly important to face environmental and energy security issues. Within bottom-up energy system modelling, two different approaches exist: one optimizes the energy mix of a selected future year, while a second optimizes the transition pathway between the current baseline and a future year. Markal/TIMES and OSeMOSYS are examples of valid modelling tools for both approaches. Due to computational issues, these models usually adopt low time resolutions and follow a time slice approach. The latter approximation is questionable given that renewables are intermittent and storage, stationary and in electric vehicles, is needed. To overcome the accuracy issue, a modelling approach based on a multi-objective evolutionary algorithm and the EnergyPLAN software, which allows for year by year simulations with an hourly time-step, was introduced. The method that includes cost decrease of technologies year by year and decommissioning of old plants is applied to the Italian energy system. The results show the importance of timing in renewables capacity expansion planning. The deepest cumulated CO2 emission reduction of the energy system operating only on residential photovoltaic, wind power and batteries is 24% while introducing electric mobility lower this value at almost 30%.

Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs.

Impact of operating rules on planning capacity expansion of urban water supply systems

ABSTRACTWater utilities often rely on industrial water supply (e.g. desalination) to complement natural resources. These climate-independent sources of supply allow operators to respond quickly to varying operating conditions, but require them to choose operating strategies, or rules. How does such operational flexibility impact the performance of water supply systems? How might it affect long-term plans for capacity expansion? Possibly significantly, as demonstrated by the analysis of a water supply system based on Singapore. First, we simulate the dynamics of the system under multiple rainfall and operating scenarios to understand the extent to which the operators’ behavior affect system performance. Results show that different operating rules can have comparable impact on the variability in system performance as hydrological conditions. Then, we show that small changes in the operating rules can lead to substantial changes in the capacity expansions, such as the size of a new desalination plant.

Do Firms Strategically Announce Capacity Expansions to Deter Entry?

Using plausibly exogenous variation in Chinese imports, we provide evidence that firms strategically announce capacity expansions when facing entry threats. We first construct and validate a novel text-based measure of voluntary disclosure that reflects firms’ explicit forward-looking statements about capacity expansion plans. We then show that firms respond to heightened entry threats by announcing capacity expansions. Consistent with our predictions, larger firms are more likely to respond in this fashion, while more opaque firms are less likely to respond in this fashion. Our results cannot be explained by an overall increase in transparency/disclosure; we observe no increase overall disclosure. Thus, our results are unlikely to be driven by investors’ demand for information, but rather firms’ strategic choices to supply information in order to protect their product markets. Capacity expansion announcements appear to be effective at deterring entry.

Electricity capacity expansion plan for Lesotho – implications on energy policy

This study aims to produce a research-based integrated electricity expansion plan for Lesotho that focuses on the security of supply at national level. The Autoregressive Integrated Moving Average (ARIMA) is used to model electrical demand and the PLEXOS modelling tool is used to analyse the cost of investing and producing future electricity for the country. The results underscore the need for investment geared towards local generation particularly in large hydro up to 0.22 GW, PV up to and 1.1 GW and pumped storage up to 0.5 GW by 2050, to keep up with future demand and reduce the cost of imported electricity in the country. Succinctly, the investigation reveals, inter-alia, that: 1) Lesotho's energy demand will continue to increase over the modelled period (up to 2050), with the gap between the local generation and demand concomitantly increasing; 2) large hydro generation, if harnessed will guarantee long-term energy security and cheaper energy relative to both imports and small hydro; 3) any shift in the energy policies of external suppliers at current tariff structures, will increase Lesotho's energy costs significantly, thus, negatively impacting on the country's economy; and 4) investing in local energy generation will guarantee long-term national energy security and affordability.

On capacity expansion planning under strategic and operational uncertainties based on stochastic dominance risk averse management

A new scheme for dealing with uncertainty in scenario trees is presented for dynamic mixed 0–1 optimization problems with strategic and operational stochastic parameters. Let us generically name this type of problems as capacity expansion planning (CEP) in a given system, e.g., supply chain, production, rapid transit network, energy generation and transmission network, etc. The strategic scenario tree is usually a multistage one, and the replicas of the strategic nodes root structures in the form of either a special scenario graph or a two-stage scenario tree, depending on the type of operational activity in the system. Those operational scenario structures impact in the constraints of the model and, thus, in the decomposition methodology for solving usually large-scale problems. This work presents the modeling framework for some of the risk neutral and risk averse measures to consider for CEP problem solving. Two types of risk averse measures are considered. The first one is a time-inconsistent mixture of the chance-constrained and second-order stochastic dominance (SSD) functionals of the value of a given set of functions up to the strategic nodes in selected stages along the time horizon, The second type is a strategic node-based time-consistent SSD functional for the set of operational scenarios in the strategic nodes at selected stages. A specialization of the nested stochastic decomposition methodology for that problem solving is outlined. Its advantages and drawbacks as well as the framework for some schemes to, at least, partially avoid those drawbacks are also presented.

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Abstract Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm. Keywords: Energy planning, generation expansion planning, capacity expansion planning, Generation expansion planning

An Evolutionary Rule-Based Framework for the Design and Management of Engineering Systems With Flexibility

Embedding flexibility in engineering systems is a promising way to improve system performance under uncertainty. However, decision-makers are often unclear when and how to exercise flexibility after it has been embedded in a system design. This paper presents an evolutionary rule-based framework to address this challenge. The proposed framework extends the traditional flexibility management framework by automatically calibrating decision rules using an evolutionary algorithm. The calibrated decision rules can be used by decision-makers to adaptively adjust decisions in response to a changing environment. By incorporating a decision rule and evolutionary algorithm, the proposed framework not only helps generate flexible designs in the system design phase, but also provides intuitive guidance for decision-makers to manage flexibility in the system operation phase. To demonstrate its application, the proposed framework is applied to generate and optimize the decision rule of capacity expansion strategy for a waste-to-energy system. The results show that the flexible design that expands capacity based on the decision rule generated by our proposed framework outperforms the design with a fixed capacity expansion plan. These findings demonstrate that the proposed framework can effectively support flexibility management in terms of improving the system’s overall expected net present value.