Generation expansion planning in the competitive environment

Computation of Loss of Load Probability (LOLP) is a challenge in Generation Expansion Planning (GEP) problem. This paper presents a dynamic GEP model considering LOLP as a reliability criterion. The objective function of the proposed dynamic GEP model includes the discounted total costs of investment cost, operation cost, and maintenance cost. The proposed LOLP constrained GEP problem is formulated as a mixed integer nonlinear programming (MINLP) problem. The MINLP formulation of GEP problem is then converted to a mixed integer linear programming (MIP) problem by applying several approximations to LOLP constraint. The utilized approximations are divided into two clusters for low and high order outages. A test case containing 12 types of installed and 5 types of candidate plants with a 14-year planning horizon is used to validate the efficiency of the proposed dynamic GEP problem. The developed MIP-based GEP model is solved using CPLEX solver and the obtained results are compared with the conventional LOLP-constrained GEP model.

Generation expansion planning with wind power plant using DE algorithm

The growth of population and increasing consumption of electricity exposes countries to build additional power units. One of the most important issues in the field of power system is the generation expansion planning (GEP) problem. The objective of the GEP problem is to evaluate the least cost investment plan for addition of power generating units over a planning period subject to power demand and energy demand constraints. In this paper an optimization model with fuzzy parameters is used to express GEP problem. In proposed model both of power demand and energy demand are taken as fuzzy parameters. A Qualitative analysis of basic stability notions for the problem of concern is defined. These notations are the set of feasible parameters, the solvability set and the stability set of the first kind. A case study is provided to demonstrate the efficiency of proposed model. A comparison study between our approach and the deterministic one is made.

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

Generation expansion planning (GEP) is a vital step in power system planning after the load forecast. The integration of renewable energy sources makes the GEP problem less reliable due to intermittent nature. The concept of exploiting the energy sources of multiple regions and cross-border power exchange is gaining immense attention in energy policy strategic planning. The aspect of regional connectivity has been incorporated in the least cost GEP by proposing two new models for large-scale power systems as intra-regional and inter-regional GEP. Intra-regional GEP simulates the composite planning for multiple zones of a region, whereas inter-regional GEP accommodates the energy sources of multiple regions and promotes the import–export of electrical power across the border. To pursue the least cost GEP, a novel meta-heuristic GEP optimization framework has been proposed in this paper. The proposed GEP optimization framework is biogeography-based optimization employing the correction matrix method-with-indicators (BBO-CMMI). In BBO-CMMI, a new parallel constraint handling approach called the correction matrix method-with-indicators (CMMI) has been developed. The proposed optimization framework is applied to reliability-constrained and emission-constrained GEP problems from the literature. The proposed framework shows promising results in terms of the least cost and runtime as compared with the results given by recent approaches presented in the literature. Similarly, the framework outperforms to optimize the large-scale power systems for intra-regional and inter-regional GEP. The applicability of the proposed approach has also been evaluated by applying to a real case study of Pakistan’s power system to devise the feasible generation expansion plan .

The Electricity Demand Prediction (EDP) and Generation Expansion Planning (GEP) make a real-world planning problem more optimal. In this study, the EDP problem has been solved using the optimization techniques such as Differential Evolution (DE), Genetic Algorithm (GA) and Artificial Immune System (AIS) for Tamil Nadu state, India. The electricity demand has been predicted and validated in terms of Mean Absolute Percentage Error (MAPE). The results reveal that DE outperformed other algorithms in solving the EDP problem. Continually, the GEP problem under partial deregulated environmental has been solved using the DE. In the GEP model, utilities and Independent Power Producer (IPP) have acted as the generating sources where IPP sells the power to utilities and utilities sell the power to the consumers. The utilities have been modeled to ensure the profit of all IPP, fuel mix ratio, system security, reliability, etc. The problem has been solved for the 6-year (till 2024) and 12-year (till 2030) planning horizons by considering three different scenarios, such as (i) without budget constraint of utilities, (ii) with budget constraint of utilities and (iii) penetration of huge Renewable Energy Source (RES) with budget constraint of utilities. The expansion capacities of each power plants were estimated over the planning horizon by maximizing the profit to the generating sources. The values of reliability indices and CO2 emission have been obtained.

The aim of the generation expansion planning(GEP) problem is to determine the type, size and time of construction of generating units to achieve minimum total costs of investment, generation, and maintenance. According to the adequacy criteria, to have a reliable power system the yearly peak load must be supplied with a predefined reserve margin. The forecasting of yearly peak load for a future time horizon has some degrees of error. To this end the generation expansion planning must be carried out to tolerate the uncertainty of yearly peak load. In this paper the GEP problem is formulated as a mixed integer non-linear programming(MINLP) model. The uncertainties of yearly peak loads is handled via the point estimate method. The proposed model is simulated over a test system is with a 14-year planning. The developed MINLP-based GEP model is solved using SBB solver and the obtained results are compared with the conventional GEP model.

The generation expansion planning (GEP) and reactive power planning problems are nonlinear dynamic optimization problems that can only be fully solved by complete enumeration, a process that is computationally impossible for realistic planning problems. Therefore, modern heuristic approaches, such as genetic algorithm (GA), are well suited for these complex planning problems. The GEP is one of the most important decision‐making activities in electric utilities. The chapter introduces an improved genetic algorithm, which can overcome the problems of conventional GA to some extent. Solving a least‐cost GEP problem is equivalent to finding a set of optimal decision vectors over a planning horizon that minimizes an objective function under several constraints. The reactive power planning problem is a nonlinear optimization problem. The planning problem is divided into operational and investment planning subproblems. The reactive power planning problem is to determine the optimal investment of VAR sources over a planning horizon.

The impacts of different bidding segment numbers on bidding strategies of generation companies

Optimization modeling to support renewables integration in power systems

The two previous chapters describe and analyze the generation expansion planning (GEP) problem as well as the joint generation and transmission expansion planning (G&TEP) problem, both from the perspective of a central planner that determines the expansion plan that is most beneficial for the system as a whole. Unlike the problems in those two chapters, referred to as centralized expansion problems, this chapter addresses a market-oriented generation investment problem, in which a single power producer competing in an electricity pool seeks to maximize its own profit through making the best generation investment decisions. It is important to note that a market-oriented decision-making problem is generally more complex than a centralized one, since the former is subject to additional uncertainty related to the behavior of other producers (rivals). In this chapter, we provide and analyze different investment models that progressively incorporate additional details with emphasis on solution techniques.

SUMMARYIn the future, renewable energy (RE) generation systems will be constructed and interconnected to the power system by nonutility entities such as generation companies and residential customers. Therefore, generation expansion planning (GEP) should consider the effects of RE generation. The purpose of this paper is to discuss the best GEP from the economic, supply reliability, and environmental aspects considering the penetration of RE generation. For this purpose, this paper proposes a model of a utility's GEP process considering RE generation. The proposed method is based on stochastic dynamic programming (SDP) in which long‐term uncertainties are modeled by geometric Brownian motion (GBM) and the binomial lattice process. The variation of RE generation output due to the weather conditions (short‐term uncertainty) is also considered in the proposed method by means of the net load curve.

This paper proposes a novel price based load frequency control scheme for a two area system, using an Unscheduled Interchange (UI) price signal, which is ideal and suitable for electricity market. The Government of India has introduced Availability Based Tariff (ABT) structure with an intention of ensuring grid security and to regulate grid indiscipline through Central Electricity Regulatory Commission Regulation. After the introduction of this regulation, the over or under injection by the generating companies (GENCOs) during off peak or peak hours are demoralized and the frequency is maintained at the nominal value. In this paper, the GENCOs instead of reacting to this price signal manually, an automated mode of frequency control is deployed in each area using UI price signal to achieve fast response to load change. The Distribution Company (DISCO) Participation Matrix (DPM) has also been employed in this work to relate the scenario under deregulation of electricity market. The proposed scheme has been verified for different cases by simulating it on a two area system, each having four GENCOs and one DISCO in MATLAB/SIMULINK environment. From the results, it is clearly observed that if the proposed method is employed by all GENCOs, it will certainly enhance the control of system frequency and at the same time throw down the UI liability of market participants. It also ensures that the GENCOs and DISCOs strictly adhere to the bilateral contract following the DISCO participation matrix.

In this paper, we propose a formulation of the unit commitment problem based on the profit under the deregulated electricity market(PBUC). We express the unit commitment problem as a mixed integer nonlinear optimization problem in which the objective is to maximize profits for generation company and the decisions are required to meet all kinds of operating constraints. Under the assumption of competitive market and price forecasting, we developed an improved discrete binary particle swarm optimization(PSO) and standard value PSO to solve this PBUC problem iteratively. A generation company with 10 generating units is used to demonstrate the effectiveness of the proposed approach. Simulation results are compared with those obtained from reference method.

Reducing air pollution and greenhouse gases is one of the most urgent issues facing countries around the world. Thermal power plants have a significant role in increasing this pollution. Due to the increase in energy consumption over the time, the power systems should prepare the adequate power generation capacity. In this paper, a Generation Expansion Planning (GEP) model is developed to minimize the cost of capacity investment, fuel cost subject to low carbon policy. In addition to thermal power plants, nuclear and renewables plants are considered as candidate for expansion. Two Low carbon policies including carbon tax and carbon allowance are considered and their impacts on the GEP solution is evaluated. The simulated study case consisting of 7 types of installed units and 9 types of candidate plants over a 24 year time horizon. is used to validate the efficiency of the proposed dynamic GEP problem. Finally, the results of all three scenarios are compared.