Long-term Planning Model Research Articles

Future thinking on power planning: A balanced model of regions, seasons and environment with a case of Taiwan

The challenges in future energy transition will be regional and seasonal nature of renewable energy and air pollution. Regional factors of air pollution involve regional equity, and only a few researches tackled long-term power planning with mitigation of air pollution. Therefore, the aim of this study is to develop a balanced long-term power planning model with compromise solutions and to decide the power generation configuration under seasonal, regional and air pollution factors in addition to cost. Based on empirical study of Taiwan, the future thinking of air pollution reduction should accept transmission from renewable energy between regions due to the cost effective and environmental benefits. The renewable energy and minor increase in non-renewable energy with better equipment will mitigate the emissions. The coal power will be the source of flexible scheduling at peak load. Interregional cooperation with seasonal power dispatching has become the future direction. A sensitivity analysis is conducted to provide suggestions when decision makers’ preference change. This study can serve as a decision support system for policymakers and a platform for stakeholders to discuss future visions on power planning. Further, this model can be applied to other countries that are experiencing economic development and environmental protection dilemmas.

A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances

Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.

Representing cross-border trade of electricity in long-term energy-system optimization models with a limited geographical scope

Long-term energy system optimization models can be designed to model systems with a broad geographical scope that comprises multiple countries. However, due to computational limitations, often the geographical scope is restricted to a single country. This raises the problem of correctly accounting for cross-border trade of electricity in models with a limited geographical scope. Therefore, this paper assesses the impact of not correctly representing cross-border trade flows in geographically restricted long-term planning models. To this end, we use a planning model for the interconnected Central-Western European power system to compare technology choices and welfare estimates for Belgium when (i) cross-border trade of electricity is ignored and (ii) cross-border trade flows are an endogenous part of the planning model. Furthermore, this paper presents two sets of methodologies to account for transmission flows in planning models. A first methodology is to extend the model’s geographical scope and fix the capacity variables in the neighboring countries in line with pre-designed scenarios for those countries. A second methodology further reduces the computational cost by using specially tailored import and export curves to represent each country’s trade opportunities. The results indicate that for highly interconnected systems, neglecting cross-border trade or having a highly stylized representation of cross-border flows can lead to inaccurate welfare estimates and technology biases. In addition, a key insight presented in this paper is that congestion rents can constitute a major share of the welfare gains attained by trading electricity. Finally, endogenizing the dispatch decisions in neighboring countries is the most accurate method to deal with cross-border trade, while by correctly designing cross-border trade curves computational time can be reduced, but planning model outcomes become less accurate.

Driving Forces Analysis of Power Consumption in Beijing Based on LMDI Decomposition Method and LEAP Model

With increasing pressure on resources and environment, sustainable development is becoming more and more important. As the largest energy consumer in the world, China needs to take measures to achieve energy transformation more urgently both from supply and demand side, which is of great significance for sustainable development and achieving carbon emissions target. In recent years, the capital city Beijing has also made great efforts to promote the replacement of electric energy in residential heating, manufacturing, transportation, power supply and consumption. In order to explore driving forces of total power consumption in Beijing`s final demand sectors, this paper decomposes the factors into industrial electricity substitution effect, industrial energy intensity effect, industrial structure effect, economic scale effect, population structure effect, residential electricity substitution effect, residential energy intensity effect and population size effect based on the logarithmic mean Divisia index (LMDI) decomposition method. The decomposition results show that the industrial electricity substitution effect made the largest contribution to increase power consumption in Beijing’s final energy consumption sector, followed by economic scale effect, residential energy intensity effect, population scale effect and residential electricity substitution effect, and other`s effect does the opposite. Finally, seven different scenarios are set up to forecast the future power consumption of Beijing`s final sectors based on the long-term energy alternative planning model (LEAP), which reveals the impact of energy efficiency improvement and electricity substitution polices on electricity consumption in Beijing`s final energy consumption sectors.

Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility

In the context of an increasing penetration of intermittent renewable energy sources, the impact of neglecting unit commitment constraints in generation expansion planning models has been widely assessed and demonstrated in the literature. However, the focus is often on thermal generators being the only source of flexibility, and the sensitivity to certain assumptions (e.g., system requirements and technical characteristics) has not been investigated. This paper contributes to the existing literature by revisiting the relevance of considering unit commitment constraints in generation expansion planning models for varying assumptions regarding both the available flexibility and the need for operating reserve requirements. The results indicate that if storage technologies are considered, integrating technical constraints has only a minor impact on both overall cost projections and most investments, with the exception of investments in storage technologies themselves. Furthermore, the investments in storage technologies are shown to be highly sensitive to the assumptions made regarding the assumed flexibility of thermal generators and the future need for operating reserves. These sensitivities are shown to be of an order of magnitude higher or of an equal order of magnitude compared to the impact of integrating technical constraints with continuous rather than integer commitment variables.

An Adaptive Robust Optimization Model for Power Systems Planning With Operational Uncertainty

There is an increasing necessity for new long-term planning models to adequately assess the flexibility requirements of significant levels of short-term operational uncertainty in power systems with large shares of variable renewable energy. In this context, this paper proposes an adaptive robust optimization model for the generation and transmission expansion planning problem. The proposed model has a two-stage structure that separates investment and operational decisions, over a given planning horizon. The key attribute of this model is the representation of daily operational uncertainty through the concept of representative days and the design of uncertainty sets that determine load and renewable power over such days. This setup allows an effective representation of the flexibility requirements of a system with large shares of variable renewable energy, and the consideration of a broad range of operational conditions. To efficiently solve the problem, the column and constraint generation method is employed. Extensive computational experiments on a 20-bus and a 149-bus representation of the Chilean power system over a 20-year horizon show the computational efficiency of the proposed approach, and the advantages as compared to a deterministic model with representative days, due to an effective spatial placement of both variable resources and flexible resources.

Can models for long-term decarbonization policies guarantee security of power supply? A perspective from gas and power sector coupling

The assessment of adequacy and security of the energy system requires the detailed knowledge of physical and operational characteristics. In contrast, studies concerning energy transitions employ stylized models that oftentimes ignore the technical properties but have a lasting influence on long-term energy policies. This paper investigates the gap between energy system planning and operational models by linking these two perspectives: (1) a long-term investment model with low spatial resolution and high level of aggregation, and (2) a spatially resolved system security model that captures the interdependences between the backbone of the electric power sector, i.e., the electricity and the gas infrastructure. We assess EU decarbonization pathways of the electricity sector towards 2050 by integrating the investment decisions of the long-term planning model and the safety performance of the resulting system operations via the security assessment model. In a large RES deployment scenario, we investigate two flexibility options: gas power plants and cross-country transmission expansion. Using the integrated model, we analyze how the adequacy and security of supply under extreme short-term operational conditions impact the long-term planning of the energy system and the investment decision-making. We provide country specific recommendations for UK. Results indicate weaknesses in the gas-electricity system and suggest improvements on capacity allocation.

The importance of time resolution, operational flexibility and risk aversion in quantifying the value of energy storage in long-term energy planning studies

This paper analyzes the impact of modeling detail in long-term energy planning models when assessing the value of energy storage in electricity markets. By running six optimization models for the long-term planning of combined generation and storage installed capacities in the Chilean electricity system (each with different levels of detail/complexity in terms of time resolution, recognition of operational inflexibility —i.e. technical constraints of power plants— and recognition of uncertainty in fossil fuel prices), we determine six portfolio solutions with significantly different levels of energy storage installed capacity. Furthermore, we found that the total installed capacity of storage plants escalates when increasing the level of modeling complexity, which can be achieved by augmenting the time resolution and the number of constraints that better recognize the inflexibility of generation plants and by acknowledging the presence of long-term uncertainties associated with fossil fuel prices fluctuations. In our particular study, we found a difference of more than an order of magnitude between the amount of installed capacity of storage plants determined by the detailed model (that with hourly resolution and full consideration of technical constraints of power plants) and that obtained by the planning model that adopts the traditional assumptions commonly utilized in regulatory offices around the word (i.e. low time resolution and no recognition of technical/unit commitment constraints and uncertainty). Particularly, we found that the traditional, simplified solution can deliver an installed capacity of storage plants as low as 240 MW (∼1.3% of estimated peak demand), while one of the most sophisticated solutions (which recognizes technical constraints of generating units, but ignores risks) delivers 7.8 GW (∼41.7% of estimated peak demand). Moreover, by running a risk-constrained stochastic planning model, we also determine a risk-averse portfolio solution, which demonstrated the increased value of energy storage capacity in reducing electricity cost risk.

Decision support for strategic energy planning: A robust optimization framework

Optimization models for long-term energy planning often feature many uncertain inputs, which can be handled using robust optimization. However, uncertainty is seldom accounted for in the energy planning practice, and robust optimization applications in this field normally consider only a few uncertain parameters. A reason for this gap between energy practice and stochastic modeling is that large-scale energy models often present features—such as multiplied uncertain parameters in the objective and many uncertainties in the constraints—which make it difficult to develop generalized and tractable robust formulations. In this paper, we address these limiting features to provide a complete robust optimization framework allowing the consideration of all uncertain parameters in energy models. We also introduce an original approach to make use of the obtained robust formulations for decision support and provide a case study of a national energy system for validation.

Multivariate simulation modelling for adaptive long-term infrastructure planning

Infrastructure asset managers are challenged by rapid changes around the world such as urbanisation and climate change. They have to deal with multiple complex and dynamic infrastructure systems that can influence each other and that can be influenced by various uncertain factors. Traditional planning methods are not well suited to complex and dynamic infrastructure environments. However, there are various alternative methods that can be used. This paper presents a simulation model for adaptive long-term infrastructure planning. For this model, a combination of planning and simulation methods is used that can cope with complex and dynamic (infrastructure) environments (i.e. system dynamics, exploratory modelling and analysis and adaptive pathways). This methodology is illustrated with a case consisting of a road system and a lock system in the Netherlands. The approach should be tested further in other cases, but it shows promise in improving the support of infrastructure decision-making in a complex, dynamic and uncertain infrastructure environment.

A multi-scale calibration approach for process-oriented aggregated building energy demand models

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL's BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

Multi-scenario comparison analysis on nuclear power development in China

In the future, nuclear power will play a much greater role in promoting energy transition, and people are retrieving their confidence of nuclear power since Fukushima nuclear accident. However, there’re many parameters will affect nuclear power development scale. In view of nuclear power investment cost increase, policy constraint of nuclear power scale and power demand rise, this paper has done sensitivity analysis on relevant indexes, such as such as power supply structure and distribution, power generation, power flow, environmental benefits and regional nuclear plant sites resource utilization rate, by use of a multi-region multi-scenario medium and long-term electric power planning model. The result shows that, nuclear power cost, power demand, carbon tax and policy guidance will affect future development scale of nuclear power.

The Impact of Neighboring Markets on Renewable Locations, Transmission Expansion, and Generation Investment

Many long-term investment planning models for liberalized electricity markets either optimize for the entire electricity system or focus on confined jurisdictions, abstracting from adjacent markets. In this paper, we provide models for analyzing the impact of the interdependencies between a core electricity market and its neighboring markets on key long-run decisions. This we do both for zonal and nodal pricing schemes. The identification of welfare optimal investments in transmission lines and renewable capacity within a core electricity market requires a spatially restricted objective function, which also accounts for benefits from cross-border electricity trading. This leads to mixed-integer nonlinear multilevel optimization problems with bilinear nonconvexities for which we adapt a Benders-like decomposition approach from the literature. In a case study, we use a stylized six-node network to disentangle different effects of optimal regional (as compared to supra-regional) investment planning. Regional planning alters investment in transmission and renewable capacity in the core region, which affects private investment in generation capacity also in adjacent regions and increases welfare in the core region at the cost of system welfare. Depending on the congestion-pricing scheme, the regulator of the core region follows different strategies to increase welfare causing distributional effects among stakeholders.

Optimal energy production from wind and hydroelectric power plants

ABSTRACTAll countries attach great importance to renewable energy investments with concern that future fossil-fueled energy resources could be exhausted. Thus, a very large renewable energy production potential may be predicted in not a very distant future. This study is about optimal energy production from wind and hydroelectric power plants at a small scale settlement center. A water resources system with multiple reservoirs in which wind power plants are located around the basin is described in this study. The system has three scenarios, in which wind and hydroelectric power plants are integrated, separated and no wind turbines. In the integrated scenario, by the energy generated in the wind power plants, the released flows from the reservoirs are sent to the reservoirs as a use again. In models of every scenario, optimal operational models for long-term planning are established on the system. The technique of dynamic programming with successive approximations is used in these models. The models are applied to a water resources system with multiple reservoirs presented successively on the main line of the Ceyhan River in the Ceyhan Basin. The results obtained here are evaluated in terms of three scenarios developed for energy production. As a result, it has been seen that the systems of the integrated and separated scenario are similar to energy productions and system without wind turbines produces more little energy production to other scenarios.

Multi‐stage bi‐level linear model for low carbon expansion planning of multi‐area power systems

This study proposes a multi-stage expansion model for coordinated transmission and generation of expansion planning of a multi-area power system (MAPS) wherein each region seeks to benefit from the changes. The proposed model adopts a bi-level optimisation approach. In the first level, the expansion cost and carbon emissions are calculated for each region separately for expansion planning. In the second level of optimisation, the calculated cost and emission values are used as the upper limits for the cost of expansion and emissions of the regions in multi-area expansion planning. The proposed bi-level approach prevents one region from bearing additional expansion costs without compensatory benefits and provides advantageous collaboration for the participant regions in the MAPS expansion. The proposed linear model requires fewer computational expansion models for long-term planning of the MAPS, but takes the uncertain generation of renewable units into account.

The impact of short-term variability and uncertainty on long-term power planning

Traditionally, long-term investment planning models have been the apparent tool to analyse future developments in the energy sector. With the increasing penetration of renewable energy sources, however, the modelling of short-term operational issues becomes increasingly important in two respects: first, in relation to variability and second, with respect to uncertainty. A model that includes both may easily become intractable, while the negligence of variability and uncertainty may result in sub-optimal and/or unrealistic decision-making. This paper investigates methods for aggregating data and reducing model size to obtain tractable yet close-to-optimal investment planning decisions. The aim is to investigate whether short-term variability or uncertainty is more important and under which circumstances. In particular, we consider a generation expansion problem and compare various representations of short-term variability and uncertainty of demand and renewable supply. The main results are derived from a case study on the Danish power system. Our analysis shows that the inclusion of representative days is crucial for the feasibility and quality of long-term power planning decisions. In fact, we observe that short-term uncertainty can be ignored if a sufficient number of representative days is included.

Modeling the Hidden Flexibility of Clustered Unit Commitment

This paper proposes a Clustered Unit Commitment (CUC) formulation to accurately model flexibility requirements such as ramping, reserve, and startup/shutdown constraints. The CUC is commonly applied in large and long-term planning models to approximate the units' operational flexibility in power systems due to its computational advantages. However, the classic CUC intrinsically and hiddenly overestimates the individual unit's flexibility, thus being unable to replicate the result of the individual UC. This paper then present a set of constraints to correctly represent the units' hidden flexibility within the cluster, mainly defined by the individual unit's ramping and startup/shutdown capabilities, including up/down reserves. Different case studies show that the proposed CUC replicates the objective function of the individual UC while solving significantly faster, between 5 to 311 times faster. Therefore, the proposed CUC correctly represents the individual unit's ramping and reserve flexibility within the cluster and could be directly applied to long-term planning models without significantly increasing their computational burden.

The impact of planning reserve margins in long-term planning models of the electricity sector

The planning reserve margin is the predominant metric used in long-term planning models to ensure the resource adequacy of projected power systems. Considerable work has been done to estimate the contribution of variable renewable energy resources, such as wind and solar, to the planning reserve margin, but little work has been done to assess what planning reserve margin should be used in planning models. Typically, U.S.-based models use the North American Electric Reliability Corporation (NERC)-recommended reserve margin levels. However, historical reserve margins have often exceeded the NERC-recommended levels, suggesting that the use of NERC-recommended levels in planning models may negatively bias projected future capacity investments relative to real-world trends. Using the Regional Energy Deployment System capacity expansion model, we show that setting the planning reserve margin to observed levels in lieu of the NERC-recommended levels leads to substantial differences in near-term capacity additions. Scenarios using alternative specifications for the reserve margin resulted in increased national solar builds of 20–100 GW. Given that the magnitude on results from altering the reserve margin level is similar to or greater than many policies frequently analyzed with planning models, careful consideration of the reserve margin level is crucial for developing accurate power sector projections.

Soft-linking energy demand and optimisation models for local long-term electricity planning: An application to rural India

Rural electricity plans are usually designed by relying on top-down rough and aggregated estimations of the electricity demand, which may fail to capture the real dynamics of local contexts. This study aims at soft-linking a bottom-up approach for short- and long-term forecasts of load profiles with an energy optimisation model in a more comprehensive rural energy planning procedure. The procedure is applied to a small Indian community, and it is based on three blocks: (i) a bottom-up model to project households' electrical appliances, which adopts socio-economic indicators to make long-term projections; (ii) a stochastic load profile generator, which employs correlations and users’ habits for assessing the coincidence and load factors; (ii) an energy optimisation model based on OSeMOSYS to find the economic optimum. The simulations show that demand models based on socio-economic indicators lead to more structured and less arbitrary scenarios. The soft-link with the energy optimisation model confirms that when accounting for short- and long-term variabilities of electricity demand together, the optimal capacities and costs can vary up to 144% and 50% respectively. Integrating optimisation tools to bottom-up models based on socio-economic indicators for forecasting electricity demand is therefore pivotal to set more reliable investments plans in rural electrification.

The feasibility of cost-effective gas through network interconnectivity: Possibility or pipe dream?

Australia's east coast gas market has faced transformational shifts in demand with the commencement of three Liquefied Natural Gas facilities in Queensland. Faced with risks of high domestic prices and potential gas shortages, political intervention has been considered as a possible solution. This paper investigates the impact of network interconnectivity on domestic gas prices by employing a long-term planning model underpinned by mathematical optimisation. At optimal system cost, improved network interconnectivity can provide material and sustained price reductions for the gas market with potential flow-on reductions to the electricity market. Increased connectivity is shown to deliver reductions of over $2/GJ in average gas prices across the eastern seaboard, with a subsequent reduction in electricity prices across all mainland National Electricity Market (NEM) regions. The results also highlight the need to unlock new supply as new transmission projects, though having the potential to reduce gas prices through market connectivity, rely on adequate supply to meet long term demand, and sustain market balance.