Long-term Planning Model Research Articles

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.

Optimal Energy Production In The Yeşilirmak River Basin

Optimal planning and operation of water resources are depend on a complex structure and process that include country economics and life standards. Currently, sustainable use and development of water resources to maximize possible benefits are the two big challenges to be overcome and efficient use of hydro power forms a significant component of this. In this study, a water resources system with multi-objectives and multiple reservoirs is described. It is considered to three scenarios in this system. The system is set up on the optimal operation model for the long term planning. The technique of the dynamic programming with successive approximations is used for the model. Objective function in the optimization model has two stage, maximization of firm and total energies. The model is applied to a water resources system with multi-objective and multiple reservoirs presented successively on the main and secondary line of the Yeşilırmak River in the Yeşilırmak River Basin. Results obtained are evaluated to the maximization of the energy production. Accordingly, the firm energy obtained from the optimal operational model for long term planning is presented approximate the value determined by empiric equations, and also the average energy in the model is % 32 better than the value determined by empiric equations. It is observed that the optimization process of the reservoirs with the highest operational storage was controlled and managed.

Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system

Demand estimation models are used for energy planning activities. Their primary function is focused on securing energy supply to final users using available resources in generation, transport and interconnection. Long-term planning models typically use non-linear optimization techniques considering an error not exceeding 5%. The reference model used by UPME in Colombia is limited to an average error of 1.6% considering non-linear modeling estimation techniques. However, they are limited in their ability to anticipate uncharacteristic variations in curves or externalities, which increases the probability of an erroneous prediction. Therefore, this research proposes a model to forecast electricity demand using neural networks in order to anticipate non-characteristic variations. The study first documents current methodologies for the prediction of maximum power demand, as well as the current deficiencies in the used forecasts, A new model is then formulated with the application of neural networks using the algorithm Cascade-Forward Back propagation using MATLAB R2017a. During the model comparison process, it was identified that the data obtained reflects the characteristics of demand behavior with an acceptable margin error equal to 0.5%.

Representation of wind power generation in economic models for long-term energy planning

The representation of wind power plants electricity generation in economic models for energy planning is problematic, since generation in wind power plants is variable and it is not possible to predict accurately enough wind fluctuations for more than a few days. Often, wind power plants generation patterns from a single historical year are repeated throughout the modelled time period. Typically, this method is used when analysing power system operation in hourly time intervals for all days in a year for each year. However, hourly time resolution is not feasible in large-scale models, which tend to require considerable amounts of computing power. Thus, some kind of time aggregation is needed. On the other hand, currently used methods for models with less than hourly temporal resolution are becoming inadequate because of increasing share of fluctuating electricity production from wind in total power generation. In this article, a methodology for evaluation of wind power plants stochastic electricity generation in power system development models is described. The methodology is based on evaluation of how much time single or multiple wind power plants generate a certain output range during a season or some time period within a year, modelling of output distribution for a typical day of the selected time period, and preparation of electricity output curves. These electricity output curves when modelling wind power plants in models with aggregated time allow to assess fluctuations in generation, observable regularities and enhance the objectivity of balancing power demand assessment, also ensure that electricity generated during a typical day corresponds to electricity generated during the selected time period. This methodology will help to determine the rational perspective power generation mix more accurately and make a better assessment of cost-effectiveness of wind power plants in economic models for energy planning, without significantly increasing the size of already large-scale models.

A mixed integer linear programming model to regulate the electricity sector

This paper presents a mixed-integer linear programming model for the optimal long-term electricity planning of the Greek wholesale generation system. In order to capture more accurately the technical characteristics of the problem, we have divided the Greek territory into a number of individual interacted networks (geographical zones). In the next stage we solve the system of equations and provide simulation results for the daily/hourly energy prices based on the different scenarios adopted. The empirical findings reveal an inverted-M shaped curve for electricity demand in Greece, while the system marginal price curve also follows a non-linear pattern. Lastly, given the simulations results, we provide the necessary policy implications for government officials, regulators and the rest of the marketers.

An integrated framework for operational flexibility assessment in multi-period power system planning with renewable energy production

This paper proposes an integrated framework for operational flexibility assessment in power system planning with a significant share of intermittent renewable energy sources (RES). The framework proposed includes: (i) the formulation of an integrated generation expansion planning (GEP) and unit commitment (UC) model accounting for key short-term technical constraints, (ii) the elaboration of accurate and systematic horizon reduction methods to alleviate the computational burden of the resulting large-sized optimization problems and (iii) the definition of suitable metrics for the operational flexibility assessment of the obtained plans. The framework is applied to a ten year planning horizon of a realistically sized case study representing the national power system of France, under several scenarios of RES penetration levels and carbon limits, spanning levels of up to 50%. The importance of incorporating the detailed short-term constraints within long-term planning models is shown. The results of the assessment show that, under high renewable energy penetration, neglecting the short-term constraints may lead to plans significantly short on flexibility, reaching shortage levels of up to 50% in frequency and several GWs in magnitude. Also, the load not served reaches levels of up to 3% and carbon emission is underestimated by up to 60%. Furthermore, the results highlight the importance of relying on suitable quantitative metrics for operational flexibility assessment in power systems planning rather than solely relying on generic performance measures, such as system costs and mixes of power plants, which are shown not to sufficiently reflect the flexibility levels of the obtained plans.

Representation of Storage Operations in Network-Constrained Optimization Models for Medium- and Long-Term Operation

This paper proposes a model to carry out analysis of storage facilities operation including a transmission network. The model represents a short-term storage operation in an approximated way that reduces computational requirements, which makes it suitable for medium- and long-term operational planning in power systems with a high level of renewable energy penetration. In the proposed model, we cluster hourly data using the so-called system-states framework developed in previous work. Within this framework, nonconsecutive similar time periods are grouped, while chronological information is represented by a transition matrix among states. We extend the system-state framework from a single-bus system to a transmission network. We define and analyze two alternative sets of representative variables for clustering hours to obtain system states when the transmission network is considered. This extension of the system states framework allows us to evaluate the impact of transmission congestions in medium- and long-term planning models in a reasonable computation time. A case study shows that the proposed model is 235 times faster than an hourly approach, used as benchmark, whereas the overall system cost is approximated with less than 2% error. The overall charging/discharging trends are similar enough to those of the hourly model, being hydro storage better approximated than fast-ramping batteries. Besides, for the analyzed case study, it is shown how congestion in the transmission network in fact improves the accuracy of the proposed approach.

Модели долговременного планирования и задача о пересечении границ

Модели долговременного планирования и задача о пересечении границ

Voltage stability constrained multi‐objective optimisation model for long‐term expansion planning of large‐scale wind farms

This study proposes a new model for long-term planning of large-scale wind farms (WFs) considering voltage stability constraints. The proposed model is a multi-objective optimisation problem which aims to maximise the profit of WF investor from wind energy procurement and to minimise the overall power generation cost. By performing modal analysis on the reduced power flow Jacobian matrix, proper locations for installation of WFs are determined. Moreover, the impact of voltage stability constraints on the capacity of WFs is investigated. In comparison with other studies, the main contributions of this study are: (i) to propose a new methodology for determination of the best locations for WFs, (ii) to study the impact of voltage stability as an important security constraint in long-term planning of wind energy. The proposed voltage stability constrained multi-objective wind power planning (VSC-MOWPP) model is solved via ɛ -constraint method in General Algebraic Modelling System optimisation package environment. The compromise solution of Pareto optimal set is selected using fuzzy satisfying approach. The proposed VSC-MOWPP model is examined on the IEEE 39-bus standard test system. The numerical investigations substantiate the suitability of the proposed model for multi-year planning of WFs at the presence of long-term voltage stability constraints.

Assessing the impact of sampling and clustering techniques on offshore grid expansion planning

Due to the ongoing large-scale connection of non-dispatchable renewable energy sources to the power systems, short- to long-term planning models are challenged by an increasing level of variability and uncertainty. A key contribution of this article is to explore and assess the implications of different dimension reduction approaches for long-term Transmission Expansion Planning (TEP) models. For the purpose of this study, a selection of sampling and clustering techniques are introduced to compare the resulting sample errors with a variety of sampling sizes and two different scaling options of the original data set. Based on the generated samples, a range of TEP model runs are carried out to investigate their impacts on investment strategies and market operation in a case study reflecting offshore grid expansion in the North Sea region for a 2030 scenario. The evaluations show that dimension reduction techniques performing well in the sampling and clustering process do not necessarily produce reliable results in the large-scale TEP model. Future work should include ways of incorporating inter-temporal constraints to better capture medium-term dynamics and the operational flexibility in power system models.

Spatial and temporal synchronization of water and energy systems: Towards a single integrated optimization model for long-term resource planning

Predictions show that pressure on already limited water and energy resources is expected to increase in many parts of the world as a result of growing populations, rapid urbanization, increasing pollution and climate change impacts. With water and energy playing a critical role in socio-economic development, ensuring resource security is a top policy concern. However, achieving this efficiently requires taking into account the various links between the two sectors through their joint management. Feedback between the water and energy sectors exists across system life-cycles and links the resources both spatially and temporally. Tracking the impacts of policies made in one sector on the other can thus be complicated and several ‘nexus’ methodologies have been developed to try and address these issues. However, the different physical, temporal and spatial characteristics of the water and energy systems present several hurdles in analyzing the two resources simultaneously. This paper overcomes many of these problems with a new, fully coupled water-energy optimization model. Based on a review of contemporary literature, the model develops an original methodology to hard-link the two systems in detail across spatial and temporal scales, as well as between individual system processes throughout the life-cycle of each resource. In addition, the model also tracks changes in water quality through each process, allowing for detailed accounting of the energy needs for water treatment. The methodology proposed in this paper can be used to investigate various cross-sectoral issues and policies such as: water availability and temperature impacts on power plant cooling; emission constraint and biofuel expansion planning impacts on water resources; and the implications of water infrastructure expansion on the energy system. The capabilities of the coupled model are investigated in an example case study for Spain. An integrated approach is shown to have several benefits including lower total costs, better resource efficiency and improved robustness for a wide range of variations in several uncertain parameters. Coupled water-energy planning thus provides a critical opportunity to improve resource security and prevent inefficient decisions which could exacerbate problems even further.

Evaluating the role of electricity storage by considering short-term operation in long-term planning

Abstract Short-term operating requirements and constraints in power systems are becoming increasingly important with the greater flexibility needed due to the integration of variable renewables. However, large problem sizes and computational barriers have limited the extent to which they are included in long-term planning models. Our objective is to understand the role of electricity storage in future renewable-based systems by including an accurate representation of short-term operation with high temporal detail within a long-term planning framework. Specifically, we discuss the development of a long-term investment model including a continuous relaxation of the technology-clustered formulation of the short-term unit commitment problem, including detailed operating reserve sizing and supply. This model is solved for a full year, and is applied to a test system with system load and renewable generation characteristics from the Belgian power system in a greenfield setting, i.e., assuming no pre-existing capacities, to analyze the role of storage at different renewable penetration levels. Both pumped-hydro storage and battery energy storage is considered, and their role in providing energy services and frequency control is investigated. We derive broadly applicable conclusions on the benefits and role of electricity storage to motivate why it may be built and operated. Results show that, in general, the integration of storage resources decreases total system cost, partially replaces flexible power plants, facilitates the integration of renewable energy sources, and allows inflexible technologies to perform better.

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.

Evaluating the Role of Electricity Storage by Considering Short-Term Operation in Long-Term Planning

Short-term operating requirements and constraints in power systems are becoming increasingly important with the greater flexibility needed due to the integration of variable renewables. However, large problem sizes and computational barriers have limited the extent to which they are included in long-term planning models. Our objective is to understand the role of electricity storage in future renewable-based systems by including an accurate representation of short-term operation within a long-term planning framework. Specifically, we discuss the development of a long-term investment model including a continuous relaxation of the technology- clustered formulation of the short-term unit commitment problem. This model is applied to a test system having similar characteristics to the Belgian power system in a greenfield setting, i.e., assuming no pre-existing capacities, to analyze the role of storage at different renewable penetration levels. Both pumped-hydro storage and battery energy storage is considered, and their role in providing energy services and frequency control is investigated. We derive conclusions on the benefits and role of electricity storage to motivate why it may be built and operated. Results show that, in general, the integration of storage resources decreases total system cost, partially replaces flexible power plants, facilitates the integration of renewable energy sources, and allows inflexible technologies to perform better.

Impact of the level of temporal and operational detail in energy-system planning models

To limit the computational cost, bottom-up long-term (LT) energy-system planning models typically have a stylized temporal representation and do not consider techno-economic operational constraints of power plants. Both these simplifications have been shown to have a significant impact on the results. However, increasing the level of temporal and techno-economic operational detail will result in an increased computational cost. In this regard, the first goal of this work is to quantify which of these simplifications has the highest impact on the results, and should therefore be prioritized for improving. To do so, the impact of both the low level of temporal and techno-economic operational detail are quantified for a varying penetration of intermittent renewable energy sources (IRES). For a high penetration of IRES, the gains obtained by improving the temporal representation are shown to outweigh the gains obtained by incorporating detailed techno-economic operational constraints. Therefore, improving the temporal representation is suggested to be prioritized. The second goal of this paper is to identify opportunities for model improvements. Different approaches to improve the temporal representation are proposed. While the focus in the literature lies primarily on the impact of the temporal resolution, this work more fundamentally considers and assesses different approaches of dealing with the temporal dimension. Using a different approach of defining the time slices to explicitly account for IRES variability is found to lead to a higher accuracy than can be obtained by simply increasing the temporal resolution, while requiring a lower number of time slices. Moreover, a temporal representation based on selecting a set of representative days can achieve an even higher accuracy, albeit requiring a higher number of time slices. An additional advantage of using representative days is that chronology is retained such that the impact of short-term dynamic fluctuations of IRES can be accounted for.

Simulating and forecasting ocean wave energy in western Canada

While the technology now exists to harvest wave energy in coastal regions, the capital expenditures for wave farms can be substantial, so it is important to be able to simulate the power in advance. Further, to integrate wave energy into the grid, utilities need to forecast over short horizons and calculate reserve requirements. Wave farms are simulated at three locations in British Columbia, Canada. Power series are calculated for six types of wave energy converters (WECs), four that operate in deep water, and two in shallow water. Forecasts are run using a physics-based model and statistical models. Five major conclusions emerge from the analysis. First, given the intermittency of buoy data, physics model hindcasts are an effective method of interpolating missing values. Second, the power output from converters does not have the same properties as the wave energy flux. Instead, the power output is a nonlinear function of the wave height and period, with fewer large outliers. Third, time series models predict well over near-term horizons while physics models forecast more accurately over longer horizons. The convergence point, at which the two types of models achieve comparable degrees of accuracy, is in the area of 2–3h in these data sets, lower than in most prior studies. The recommendation is to use time series methods to forecast at the horizons required for reserves, and physics models for long-term planning. Fourth, the predictability of the power output can differ substantially for individual converters. Finally, wave energy is found to be significantly less costly in terms of reserves than wind and solar.

A decision support model for long-term course planning

Assisting students to prepare long-term course plans towards timely graduation is a challenging and time-consuming advising activity. Unless appropriate decisions are made, undesirable consequences may occur. Earlier, an approach called Interactive Decision Support for Course Planning (IDiSC) was developed. IDiSC performs a pro-active analysis of the impact of the different aspects of the problem and eventually suggests a study plan that balances students' preferences and advisors' recommendations, without violating any regulations. IDiSC employs mathematical optimization and follows an evolutionary and iterative decision support framework, called EVOLVE*.This paper proposes IDiSC+, which is an improvement over IDiSC. The contribution of IDiSC+ is twofold. First, in order to address uncertainties and implicit concerns not formally described in the original problem statement, IDiSC+ adopts a diversification algorithm that, instead of providing a single study plan, generates a set of optimal or near optimal alternative plans that are of a similar quality and yet structurally different. Human intelligence is then employed to analyze these alternatives and to either approve one of them or refine the problem settings for generating further solutions. Second, IDiSC+ uses a more advanced and representative formal model that overcomes key assumptions and limitations of its predecessor and thus addresses the problem in a more realistic way. A real-world application of IDiSC+ is presented in order to illustrate its added value.

An integrated stochastic multi-regional long-term energy planning model incorporating autonomous power systems and demand response

The power sector faces a rapid transformation worldwide from a dominant fossil-fueled towards a low carbon electricity generation mix. Renewable energy technologies (RES) are steadily becoming a greater part of the global energy mix, in particular in regions that have put in place policies and measures to promote their utilization. This paper presents an optimization-based approach to address the generation expansion planning (GEP) problem of a large-scale, central power system in a highly uncertain and volatile electricity industry environment. A multi-regional, multi-period linear mixed-integer linear programming (MILP) model is presented, combining optimization techniques with a Monte Carlo (MCA) method and demand response concepts. The optimization goal concerns the minimization of the total discounted cost by determining optimal power capacity additions per time interval and region, and the power generation mix per technology and time period. The model is evaluated on the Greek power system (GPS), taking also into consideration the scheduled interconnection of the mainland power system with those of selected autonomous islands (Cyclades and Crete), and aims at providing full insight into the composition of the long-term energy roadmap at a national level.

Interval linear programming model for long-term planning of vehicle recycling in the Republic of Serbia under uncertainty.

An interval linear programming approach is used to formulate and comprehensively test a model for optimal long-term planning of vehicle recycling in the Republic of Serbia. The proposed model is applied to a numerical case study: a 4-year planning horizon (2013-2016) is considered, three legislative cases and three scrap metal price trends are analysed, availability of final destinations for sorted waste flows is explored. Potential and applicability of the developed model are fully illustrated. Detailed insights on profitability and eco-efficiency of the projected contemporary equipped vehicle recycling factory are presented. The influences of the ordinance on the management of end-of-life vehicles in the Republic of Serbia on the vehicle hulks procuring, sorting generated material fractions, sorted waste allocation and sorted metals allocation decisions are thoroughly examined. The validity of the waste management strategy for the period 2010-2019 is tested. The formulated model can create optimal plans for procuring vehicle hulks, sorting generated material fractions, allocating sorted waste flows and allocating sorted metals. Obtained results are valuable for supporting the construction and/or modernisation process of a vehicle recycling system in the Republic of Serbia.

A Hierarchical Framework for Long-Term Power Planning Models

In this paper, we formulate a long-term planning model of transmission line expansion based on balancing investment cost and reducing consumer cost. We achieve this by formulating a hierarchical framework that is sensitive to different agents operating on different timelines, the relationships of which may be competitive, cooperative or somewhere in between. The advantage of this framework is that while it captures the complexity of long-term decision making, it maintains clarity of information flow between models, agents and timelines. For our purposes, we introduce an equilibrium model that combines grid operational concerns with the short-term competitive behavior of generation firms. An iterative solution technique is proposed to provide a Nash solution where each optimization problem is solved globally. This solution is then used to inform an overarching transmission planning model that we solve using derivative-free optimization. Using a de-congested network as a benchmark, numerical results indicate a non-alignment of the objectives of planning and operational entities, whereby easing line congestion may not offer monetary benefits to the wholesale consumer.