Optimal Generation Expansion Research Articles

Long-Term Optimal Generation Expansion Planing considering CO2 Reduction Policies and Mechanisms

In a deregulated power system, a well-designed market mechanism could promote the optimal distribution of resources within the system. As one of the major sources of CO2 emissions, power industry can be seriously impacted by carbon emission related policies and regulations. This paper considers three widely promoted emission policies, using a long-term power system planning model to obtain the optimal generation expansion plan for twenty years. By analysing and comparing system investment schemes under different emission-related mechanisms, the processes of how these policies influence generation investment are revealed. The effectiveness of these policies is compared to determine the optimal carbon emission mechanism.

Optimal generation expansion planning model for solar PV generation on Run of River-based hydro-based power system using binary genetic algorithm

Abstract This paper presents a binary genetic algorithm approach developed to solve the optimal generation expansion planning problem for solar photo-voltaic (PV) based Run of River (ROR) hydro generation systems. The results show that the methodology effectively solves mixed-integer, constrained nonlinear generation expansion problems. The model is then used to optimize the generation expansion problem for the electricity grid in the eastern region of Nepal, considering the under construction and planned solar PV and ROR-based hydropower plants. The optimal problem is formulated to determine the optimal number, location and type of power plant to fulfill the demand for the electricity grid of the eastern zone grid of Nepal. Results show that the total generation from the optimal sites combining hydro and solar sites is found to be about 26% of that of total energy in the Nepalese grid. However, the total demand in the system under consideration for the year is about 1352 GWh. With eliminating line losses, a surplus generation of about 1377 GWh has been found. This surplus can be used as power pooling with neighboring countries. The system has a surplus during the wet season but needs to be improved during the dry season. Hence, appropriate policies can be formulated for power banking and pooling to balance the demand–supply in the system.

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

A two‐stage simulation‐based framework for optimal resilient generation and transmission expansion planning

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems

With the increasing penetration of renewable generating units, especially in remote areas not well connected with load demand, there are growing interests to co-optimize generation and transmission expansion planning (GTEP) in power systems. Due to the volatility in renewable generation, a planner needs to include the operating decisions into the planning model to guarantee feasibility. However, solving the GTEP problem with hourly operating decisions throughout the planning horizon is computationally intractable. Therefore, we propose several spatial and temporal simplifications to the problem. Built on the generation expansion planning (GEP) formulation of Lara et al. (2018), we propose a mixed-integer linear programming formulation for the GTEP problem. Three different formulations, i.e., a big-M formulation, a hull formulation, and an alternative big-M formulation, are reported for transmission expansion. We theoretically compare the tightness of the LP relaxations of the three formulations. The proposed MILP GTEP model typically involves millions or tens of millions of variables, which makes the model not directly solvable by the commercial solvers. To address this computational challenge, we propose a nested Benders decomposition algorithm and a tailored Benders decomposition algorithm that exploit the structure of the GTEP problem. Using a case study from Electric Reliability Council of Texas (ERCOT), we are able to show that the proposed tailored Benders decomposition outperforms the nested Benders decomposition. The coordination in the optimal generation and transmission expansion decisions from the ERCOT study implies that there is an additional value in solving GEP and TEP simultaneously.

Climate‐aware generation and transmission expansion planning: A three‐stage robust optimization approach

In this paper, we propose a three-stage robust generation and transmission expansion planning model considering generation profiles of renewable energy sources (RES) affected by different long-term climate states. Essentially, we extend the broadly utilized two-stage modeling approach to properly consider partial information of climate states with conditional short-term scenarios of RES output and outages. The proposed model is formulated as a five-level optimization problem. The first level determines the optimal generation and transmission expansion plan under uncertainty in climate conditions, RES generation, and contingencies. Given the selected expansion plan, the second level identifies the most severe climate state. Following the decision-information hierarchy, in the third level, the system operator optimizes the generation schedule of energy and reserves under perfect information of the climate state, but yet under uncertainty in the RES generation and contingencies. Then, the fourth level identifies the worst-case combination of contingency and conditional short-term RES generation adjusted to the current climate condition. Finally, the fifth level determines the optimal redispatch of reserves to react against the worst-case RES generation and contingency scenario considering the uppermost decisions. Within this multi-level structure, the optimal investment plan considers a more realistic decision setting, where system operators adapt RES forecasts based on the observed climate conditions before planning the operational schedule. To solve the problem, a variant of the nested column-and-constraint-generation algorithm is proposed with global-optimality guarantee in a finite number of steps. A case study based on the Chilean system illustrates the applicability of the model in a realistic network.

Optimal generation expansion planning model of a combined thermal–wind–PV power system considering multiple boundary conditions: A case study in Xinjiang, China

With the large-scale grid connection of wind and photovoltaic power, the contradiction between renewable energy and thermal power is becoming more and more serious. To solve this problem, this paper comprehensively considers the construction and operation costs of power plants, and constructs an optimal generation expansion planning model of a combined thermal–wind–photovoltaic​ power system with the objective of minimizing total cost. The planning is calculated under the boundary conditions of ensuring the safe operation of the power grid and taking into account the share requirement, utilization requirements and construction requirements for renewable energy. The model is applied to the self-use and supporting power plants in Xinjiang. The results show that the proposed generation expansion plan can realize the coordinated development of three types of power sources under multiple boundary conditions, which has good applicability.

Robust generation expansion planning considering high penetration renewable energies uncertainty

AbstractThe proper optimal generation expansion planning (GEP) should meet the reliability criteria requirements over a planning horizon under the presence of uncertainties. The intermittent nature of renewable energy sources (RES) introduces an enormous uncertainties impact within the planning model. A simulation model for RES uncertainty is developed using the capacity factor (CF) of the RES historical data. The RES simulation model is handled via the probability density function (PDF). The uncertainty parameter of different RES is described as a flexible polyhedral uncertainty set and incorporated within the proposed GEP model. The influence of different uncertainty scenarios for each RES uncertainty on the GEP model can be analyzed separately. The RES uncertainty scenarios are predefined and incorporated within the proposed GEP model through a proposed parameter named as a confidence level. The proposed confidence level parameter is beneficial to the power system planner to control the degree of robustness. Different GEP results are presented for various RES uncertainty scenarios. Three methods are proposed as appropriate solutions to deal with the RES uncertainty impact. The most economical method among the three proposed methods is determined by developing an objective function tailored to achieve the optimality of the economic factor.

Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.

A new quantitative load profile measure for demand response performance evaluation

This article proposes a novel quantitative measure of the load profile that can be used to compare demand response techniques for load shaping. This quantitative measure is a projection of the overall cost for electric power generation planning, and can be used to more effectively guide the determination of dynamic electric energy retail pricing tariffs, which can, in turn, improve the performance of demand response techniques from the overall generation expansion planning and utilization cost perspective. Traditionally, the peak-to-average ratio has been the popular choice to quantitatively measure the load profile, although it does not incorporate critical measures such as minimum power demand and load variation. In this paper, several load profiles are synthesized and their corresponding electricity supply cost is calculated. Then, the correlation coefficient between different statistical measures of the load profiles and the cost of their optimal generation expansion planning and utilization is measured. Several statistical factors of the load profile with a linear relationship with the overall cost are selected and their contribution level to the final measure is determined to form a quantitative load profile measure that accurately reflects the overall supply cost.

A vulnerability-constrained quad-level model for coordination of generation and transmission expansion planning under seismic- and terrorist-induced events

In this paper, the authors present a new vulnerability-constrained model for coordination of generation and transmission expansion planning (CGTEP) under seismic- and terrorist-induced events. The proposed planning model is outlined by a quad-level optimization problem with the aim of heightening the resilience of the power grid in reaction to these catastrophic failures. The first level represents remedial corrective actions, as short-term independent system operator (ISO)’s reaction after the seismic- and terrorist-induced events, to apply generation re-dispatch and grid re-configuration. The second level rigorously models and assesses the seismic- and terrorist-induced events, as non-random uncertain events, and their subsequent devastating effects through a modified scenario generation procedure (MSGP). In the third and fourth levels, however, the resilient optimal generation and transmission expansion plans, as the long-term ISO’s reaction after the seismic- and terrorist-induced events, are characterized by using a hybrid of the CGTEP and transmission switching devices allocation (TSDA). To solve the proposed large-scale mixed-integer nonlinear quad-level model, a potent symphony orchestra search algorithm (SOSA) was widely employed. The SOSA is an optimization technique recently developed by the authors, which inspires the organization and interactive relationship among members of a symphony orchestra. The proposed planning model was implemented on the modified 400-kV 52-bus Iranian power grid to represent the feasibility and effectiveness of the newly developed model.

Effect of Climate Change on wind speed and its impact on optimal power system expansion planning: The case of Chile

This work assesses the changes in power capacity expansion decisions regarding power generation and transmission that occur when the effects of Climate Change on wind speed are captured in the decision model. Considering an 85-year period (2016–2101), we use a Mixed-Integer Linear Program (MILP) model to analyze the optimal power capacity expansion in diverse types of power generation technologies, throughout the years and geographical locations. The optimization model minimizes the total (investment and operational) costs of the power system subject to several technical and economic constraints. We implement our model using the main Chilean power system. We compare two scenarios: one assuming that Climate Change affects wind speeds and hence wind farm capacity factors and the other assuming it does not. Our results reveal that, when taking into account the impact of Climate Change on wind speed, the optimal power generation and transmission expansion plan is different than when ignoring this effect. The variation of wind speed affects not only wind power capacity installed, but also other-technology power capacity installed. In particular, power capacity installed in wind and solar generation plants is higher (measured in MW installed) than the power capacity installed when we ignore the effects of Climate Change; and power capacity installed in diesel and natural gas technologies are lower. We perform sensitivity analyses, changing power capacity expansion limits and the discount rate, to check for the robustness of our results.

Does risk aversion affect transmission and generation planning? A Western North America case study

We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models.

Does Risk Aversion Affect Transmission and Generation Planning? A Western North America Case Study

We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models.

Generating adequacy analysis of Mexico’s national interconnected power system

Purpose Long-term reliability analysis of generation capacity based on the forecasted load demand helps to identify the optimal generation expansion plan of the system. This paper analyzes the generation adequacy of Mexico’s National Interconnected Power System (MNIPS) using loss of load expectation (LOLE) and loss of energy expectation (LOEE) indices. Design/methodology/approach These indices are calculated through an analytical (recursive) method and are then compared with values recommended by the North American Electric Reliability Council (NERC). Weekly indices are computed to analyze the load curtailment options that may occur in some periods. Findings Forecasted values, including load and generation capacity considering maintenance schedules, additions of new generating units and permanently shut down units in accordance with the long-term expanding-system plan have been considered. The load forecast uncertainty is also included. Originality/value This is original work.

Моделювання та оцінка сценаріїв довгострокового розвитку атомної енергетики України

The paper presents the results of modeling and comparative analysis of long-term scenarios of the expansion of the generating capacities of Ukraine's Power System assuming decommissioning or extension of nuclear power plants' (NPPs) life. Development of scenarios and model calculations was carried out using the optimization modeling tool WASP-Ukraine, developed on the basis of IAEA software WASP-IV designed to find the economically optimal generation expansion policy within user-specified constraints. The model WASP-Ukraine is based on linear programming technique. Modeling baseline and alternative scenarios made for the period up to 2030 and the base year is 2014. The study examined six scenarios assuming decommissioning and extension of NPP life in accordance with international practice. The share of nuclear power in electricity production in Ukraine in 2030 under different scenarios varies from 11% to 48%. In the scenarios, which minimize the role of nuclear power, compensation of decommissioned NPP is provided by renewables or by conventional thermal power plants. Comparative analysis of the scenarios is conducted by four criteria: cost efficiency (average production cost of electricity generated in a power system); environmental sustainability (total emissions of sulfur oxides (SO2) and nitrogen oxides (NOx)); reliability of the power system and electricity supply (aggregated loss of load probability (LOLP) and the energy not served (ENS) indicators); provision of power plants with primary energy resources (steam coal). The comparative analysis proved that scenarios assuming extension of NPP life are more acceptable and efficient by all selected criteria comparing to those assuming decommissioning of NPP after their life period. Thus, maintaining the dominant role of nuclear energy in Ukraine's power balance at least until 2030 is justified.

Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model

This paper presents a new uncertainty handling framework for optimal generation expansion planning (GEP) amalgamating the notions of single-stage and two-stage robust optimization (RO). The proposed multiyear robust GEP methodology, as a tractable mixed integer linear programming optimization problem, copes with the inherent planning uncertainties associated with forecasted electricity load demand, as well as estimated investment and operation costs through distribution-free bounded intervals producing polyhedral uncertainty sets. The optimal generation expansion plan obtained from the proposed RO approach is immunized against worst-case planning uncertainties considering the adopted degree of conservatism for each uncertainty set. Therefore, the proposed methodology is capable of controlling the robustness of the optimal investment schedule regarding the enforced planning uncertainties. Simulation results demonstrate the efficacy and efficiency of the proposed RO framework throughout GEP studies.

Optimal generation expansion planning considering maximum loadability limit

This paper describes a new approach to generation expansion planning (GEP) problem to optimise simultaneously multiple objectives such as cost and stability margin. It is formulated as a combinational optimisation problem in order to determine the type and capacity of the new generations as well as their timing and placement. This study presents a comprehensive model of hierarchical optimisation problem to solve the GEP problem. The proposed method has investigated cost index and security margin of voltage stability of power systems by using loadability index. In this new approach, the GEP is done via expansion coordination pattern, and a trade-off between cost and stability has taken place. To solve the modelled optimisation problem, the imperialist competitive algorithm is used and tested on two test (standard) power systems consisting of 30 and 57 buses for a 15-year planning horizon. The results have been compared with hybrid algorithm genetic-particle swarm.

Prospective scenarios for the adoption of sustainable power generation technologies in United Arab Emirates

In this work, an optimisation analysis is carried out in order to estimate the optimal power generation expansion strategy, using sustainable power generation technologies for the United Arab Emirates (UAE). Eight alternative configurations of sustainable power generation systems, such as, the integration of carbon capture and storage (CCS) technologies and solar-based power generation systems are examined and compared with the business as usual (BAU) scenario for a range of natural gas price. Based on the input data and assumptions made, simulations indicate that the BAU scenario (i.e. expansion of the power system using conventional power generation technologies) is the least cost option. However, in the case UAE will move towards the use of sustainable power generation technologies, for the reduction of carbon dioxide emissions, the natural gas combined cycle technology integrated with CCS systems and the use of concentrated solar power systems with 24/7 operation are the most suitable alternative technologies.

Identification of prospective locations for generation expansion with least augmentation of network

With ever increasing demand for electric energy, additional generation and associated transmission facilities has to be planned and executed. In order to augment existing transmission facilities, proper planning and selective decisions are to be made whereas keeping in mind the interests of several parties who are directly or indirectly involved. Common trend is to plan optimal generation expansion over the planning period in order to meet the projected demand with minimum cost capacity addition along with a pre-specified reliability margin. Generation expansion at certain locations need new transmission network which involves serious problems such as getting right of way, environmental clearance etc. In this study, an approach to the citing of additional generation facilities in a given system with minimum or no expansion in the transmission facility is attempted using the network connectivity and the concept of electrical distance for projected load demand. The proposed approach is suitable for large interconnected systems with multiple utilities. Sample illustration on real life system is presented in order to show how this approach improves the overall performance on the operation of the system with specified performance parameters.