Long-term Generation Expansion Planning Research Articles

Assessing Potential Scenarios for Achieving New and Renewable Energy Targets in Java-Bali Power System, Indonesia

Geographic circumstances, government policies, and power system characteristics face many countries struggling to achieve their new and renewable energy (NRE). In addition, one characteristic of renewable energy (RE) which cannot be moved is a severe problem for archipelagic countries like Indonesia in achieving their NRE targets. Therefore, this research creates a long-term open-source generation expansion planning (GEP) model that considers renewable energy integration between islands, government policies, and power system characteristics of Indonesia. The model proposes a high voltage direct current (HVDC) line to facilitate abundant energy transfer between islands. The research also included multiple scenario analyses based on the potential strategies that could realistically be applied. Based on the long-term GEP model results, possible alternative routes to achieving NRE targets are mapped and assessed by considering power system characteristics and national energy policies. Specifically, the Java-Bali system of Indonesia is employed as a case study to demonstrate the performance of the proposed long-term GEP model. The optimum planning to achieve the targets produces the generation cost of 7.05 cents USD/kWh and the CO2 emission reduction of 2,297 million tons of CO2.

Technological learning modelling towards sustainable energy planning

PurposeObviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.Design/methodology/approachTo the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.FindingsAs seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.Originality/valueTo the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

A multi-objective framework for long-term generation expansion planning with variable renewables

The growing importance of operational flexibility in generation expansion planning with increased integration of variable renewables has been regularly highlighted in recent research. Yet, operational flexibility has been largely overlooked in order to reduce the prohibitive problem size that results when operational details at small timescales are included in this long-term exercise. In this work, we present a multi-objective optimization framework that effectively and tractably incorporates flexibility screening of candidate generation portfolios in long-term generation expansion planning. Operational flexibility is considered as a separate objective along with the traditional economic and environmental objectives. The ability of the proposed methodology to provide valuable insights into the correlations between flexibility, total costs and carbon emissions is demonstrated using a case study. The results clearly reveal that omission of flexibility from the framework gives rise to deficient generation mixes that are unable to match the more frequent and steeper variations in net load. A high-level evaluation of the flexibility needed in generation portfolios to balance net loads with different degrees of variability is also provided. Finally, a procedure is proposed to support the decision-making process for selecting the most appropriate investment plan among the many solution options provided by the multi-objective optimization framework.

A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning

ABSTRACTIn this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.

Dynamic long-term expansion planning of generation resources and electric transmission network in multi-carrier energy systems

In this paper, a new framework is proposed for long-term generation and transmission expansion planning in multi-carrier energy systems (MCES). The MCES considered here consists of combined heat and power (CHP), gas furnace, power generation unit and transmission lines associated to natural gas and electrical networks. In the proposed framework, by minimizing the total investment and operation costs, optimal capacity, location and time of installing of new heat and electrical generation resources and also electric transmission lines are determined in a multi-year horizon. A linearized AC load flow equations is used for modeling effects of electric transmission network and is compared with DC load flow model. Also, a linearized model of accurate gas flow equations in natural gas transmission pipelines is used and is compared with a simple model. By using linear models for energy transmission network, the expansion problem is converted to a mixed integer linear programming (MILP) problem. By solving the MILP model by GAMS in which mathematical algorithm is used, optimal operation and expansion strategies on heat and power generation resources as well as electric transmission lines are obtained over the planning horizon. Performance of the proposed model is evaluated through two system tests, where transmission losses, overall system efficiency, reliability of supply and emissions are considered as metrics. Simulation results show importance of energy transmission network modeling in investment and operation of MCES in the long-term.

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets’ planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints

This work presents a generic mixed integer linear programming (MILP) model that integrates the unit commitment problem (UCP), i.e., daily energy planning with the long-term generation expansion planning (GEP) framework. Typical daily constraints at an hourly level such as start-up and shut-down related decisions (start-up type, minimum up and down time, synchronization, soak and desynchronization time constraints), ramping limits, system reserve requirements are combined with representative yearly constraints such as power capacity additions, power generation bounds of each unit, peak reserve requirements, and energy policy issues (renewables penetration limits, CO2 emissions cap and pricing). For modelling purposes, a representative day (24h) of each month over a number of years has been employed in order to determine the optimal capacity additions, electricity market clearing prices, and daily operational planning of the studied power system. The model has been tested on an illustrative case study of the Greek power system. Our approach aims to provide useful insight into strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level by providing the optimal energy roadmap under real operating and design constraints.

Agent-Based Modeling for Studying the Impact of Capacity Mechanisms on Generation Expansion in Liberalized Electricity Market

This paper presents an approach to solve the long-term generation expansion planning problem of the restructured electricity industry using an agent-based environment. The proposed model simulates the generation investment decisions taken by a particular agent (i.e. a generating company) in a market environment taking into account its competitors’ strategic investment. The investment decision of a particular company is modeled taking into account that such company has imperfect foresight on the future system development hence electricity prices. The delay in the construction of new plants is also explicitly modeled, in order to compute accurately the yearly revenues of each agent. On top of a conventional energy market, several capacity incentive mechanisms including capacity payment and capacity market are simulated, so as to assess their impact on the investment promotion for generation expansion. Results provide insight on the investment cycles as well as dynamic system behavior of long-term generation expansion planning in a competitive electricity industry.

Alternative Energy Technologies for Long-term Power Generation Expansion Planning and CO2 Mitigation in Thailand

In 2009 electricity production in Thailand was heavily dependent on fossil fuels (more than 92.73%). Domestic natural gas fields are a plentiful energy resource but it is projected to be available only for the next decade. Using domestic natural gas would reduce national energy security. However, coal will dominate in electricity generation in Thailand due to its low price. Utilization of coal results in increasing CO2 emissions. To comply with the low-carbon society policy of the Royal Thai Government, a high target of achievement in alternative energy share of 20.40% in total energy consumption in 2022 has been proposed. The obvious answer to this challenge is to take advantage of Thailand as an agriculture-based country, especially in domestic renewable energy and clean power generation. Thailand’s renewable power development plan strategy is considered by increasing the share to 2.40% in 2024. Electricity generation in this scenario is compared with future fossil-based power plants with carbon capture and storage and nuclear power in the long-term energy planning. Finally, the low carbon socity scenario for the emission abatement policy is introduced to mitigate emissions. Results are presented in total supply system cost, the incremental cost of CO2 mitigation and the co-benefits of greenhouse gas mitigation when compared to the business-as-usual scenario. Finally, Thailand could reduce CO2 emissions from the power sector by 15.27% in 2026.

The Development of the Generation Expansion Planning Model Considering Environmental Policies

In response to the global environmental concerns, diverse mechanisms have been developed which have an influence on the power industry, especially generation mix. Therefore, long-term Generation Expansion Planning (GEP) model should consider the environmental factors while ensuring reliable and efficient supply of forecasted demand. This paper proposed the multi-period GEP model that capturing the effect of Renewable Portfolio Standards (RPS) program and Emission Trading (ET), and Target Scheme (TS). Numerical results show that current generation mix in Korea solely cannot ensure the environmental requirement properly. Moreover, appropriate investment of renewable are needed.

Scenario analysis of the new energy policy for Taiwan's electricity sector until 2025

For this study, we constructed the following three case scenarios based on the Taiwanese government's energy policy: a normal scenario, the 2008 “Sustainable Energy Policy Convention” scenario, and the 2011 “New Energy Policy” scenario. We then employed a long-term Generation Expansion Planning (GEP) optimization model to compare the three case scenarios' energy mix for power generation for the next a15 years to further explore their possible impact on the electricity sector. The results provide a reference for forming future energy policies and developing strategic responses.

Scenario construction and reduction applied to stochastic power generation expansion planning

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduction heuristic in the context of long-term power generation expansion planning. We generate two different sets of scenarios for future electricity demands and fuel prices by statistical extrapolation of long-term historical trends. The cardinality of the first set is controlled by employing increasing length time periods in a tree structure while that of the second set is limited by its lattice structure with periods of equal length. Nevertheless, some method of scenario thinning is necessary to achieve manageable solution times. To mitigate the computational complexity of the widely-used forward selection heuristic for scenario reduction, we customize a new heuristic scenario reduction method named forward selection in wait-and-see clusters (FSWC) for this application. In this method, we first cluster the scenarios based on their wait-and-see solutions and then apply fast forward selection within clusters. Numerical results for a twenty year generation expansion planning case study indicate substantial computational savings to achieve similar solutions as those obtained by forward selection alone.

Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach

This paper presents an application of genetic algorithms (GA) for solving the long-term power generation expansion planning (PGEP) problem, a highly constrained nonlinear discrete optimization problem. The problem is formulated into a mixed integer nonlinear programming (MINLP) program that determines the most economical investment plan for additional thermal power generating units over a planning horizon, subject to the requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations. Computational results show that the GA-based heuristic method can solve the PGEP problem effectively and more efficiently at a significant saving in runtime, when compared with a commercial optimization package. Copyright © 2005 John Wiley & Sons, Ltd.

A New Technique for Forecasting Hourly Load for Long-Term Planning

An electric utility should plan its system well ahead of time to meet future customer demand.. It is essential to an appropriate long-term generation expansion plan, with a usual duration of 20 to 25 years, that the forecasted load be close to the demand that will occur at each moment of the planning period. The authors present a methodology to forecast the hourly load or load for smaller interval of time for long-term generation expansion planning. The methodology is based on the relation between the forecasted annual/seasonal peak load and the probability density functions (PDFs) of different load ratios. The article validates the proposed methodology by applying it to the power system of Bangladesh Power Development Board.

Economic evaluation of private power production under uncertainties

Private power production is becoming an increasingly important source of electricity generation. In developing countries, build–operate–transfer (BOT) arrangement has emerged as a dominant form of private investment. Pricing private power production at its avoided cost is the breakeven point for the utility in economic evaluation, and uncertainties must be taken into account. In this paper, an approach of calculating the breakeven cost to the utility of a BOT power plant whose contract lasts for 10–25 years is proposed. The proposed approach requires the computation of production costs from long-term generation expansion planning (GEP) under future uncertainties. To facilitate the inclusion of constraints introduced by BOT plants in GEP and uncertainties, a genetic algorithm method is utilized in GEP. The breakeven cost is a useful measure in the economic evaluation of BOT power plants. An example is presented to illustrate the economic evaluation of BOT plants using the concept of breakeven cost.

Environmental Aspects of Power Generation

This article presents a new approach to developing proper strategies for multicriteria medium- and long-term power generation expansion planning. The desired strategies present a predetermined number of appropriate planning alternatives, which should be selected from a vast initial set in accordance with the given multiple criteria. These multiple criteria, caused by considering environmental criteria (such as SO2, NOx, and particulate emissions minimization) together with economic and other criteria, make multicriteria analysis a cornerstone of the proposed approach. The proposed multicriteria analysis includes determining a criteria assessment vector for each initial alternative and multicriteria optimization of a set of such initial criteria assessment vectors. The basic issue of this article is to reflect development of calculation models for the environmental criteria (to create the criteria assessment vectors) together with appropriate environmental investigations. Suitable solution methodology and software, applied to the conditions prevailing in Israel, have been developed for the proposed approach.

A hybrid genetic algorithm/dynamic programming approach to optimal long-term generation expansion planning

This paper describes an application of a hybrid approach to optimal generation expansion planning (GEP) using a refined genetic algorithm (GA) and the tunnel-based dynamic programming (DP). Long-term GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally infeasible in a real-world GEP problem. For this reason, commercial packages have searched a reduced solution space or applied mathematical programming algorithms, which result in being stuck in a local optimum. This paper proposes a hybrid approach combining a refined GA with the tunnel-based DP, a method employed in the Wien Automatic System Planning Package (WASP). The main advantage of this approach lies in the GA's capability to find the global optimum and the tunnel-based DP's high performance to get a local optimum. The framework developed can simultaneously overcome the “curse of dimensionality” and a local optimal trap inherent in the conventional mathematical programming approaches. The suggested method has been successfully applied to two test systems with 15 existing power plants, five types of candidate plant and a 14-year planning period, and a practical long-term system with a 24-year planning period.

Forced-outage rates of generating units based on expert evaluation

The uncertainties relating to the forced outage rates (FOR) of power system generating units are important in studies such as long-term generation expansion planning. These uncertainties might be better understood by experts in the field. Expert evaluations can be modeled with fuzzy set theory, and the fuzzy-FOR so calculated reflects the conditions of operation and maintenance of the units more realistically.

Fuzzy Decision-Making on Electric Energy Strategy for Long-term Generation Expansion Planning

In an electric utility, it would be necessary to predict and make a future view of electric energy strategy for long-term generation expansion planning. The planning problem of energy strategy is formulated as a decision problem of electric generation mix in a future horizon year. This problem is a sort of multiobjective planning problems under several constraints of which parameters are uncertain. The objectives and constraints need not be fulfilled rigorously but the violation tolerated by a decision-maker. That Is, they can be regarded as fuzzy objectives and fuzzy constraints, this paper presents the fuzzy decision analysis of strategic generation mix planning problems using a fuzzy linear programming technique and some numerical examples for a future power system are demonstrated.