Generation Transmission Expansion Research Articles

Multi-period generation-transmission expansion planning with an allocation of phase shifter transformers.

Multi-period generation-transmission expansion planning with an allocation of phase shifter transformers.

Integrated generation-transmission expansion planning considering power system reliability and optimal maintenance activities

This paper evaluates lines repair and maintenance impacts on generation-transmission expansion planning (GTEP), considering the transmission and generation reliability. The objective is to form a balance between the transmission and generation expansion and operational costs and reliability, as well as lines repair and maintenance costs. For this purpose, the transmission system reliability is represented by the value of loss of load (LOL) and load shedding owing to line outages, and generation reliability is formulated by the LOL and load shedding indices because of transmission congestion and outage of generating units. The implementation results of the model on the IEEE RTS show that including line repair and maintenance as well as line loading in GTEP leads to optimal generation and transmission plans and significant savings in expansion and operational costs.

A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting

Reliable long-term hourly load forecasting is imperative for electricity utilities and planners' decision-making, especially in generation-transmission expansion planning. This paper develops a data-driven knowledge-based system for regional hourly load forecasting in the long-term horizon with contributions in both knowledge base and inference engine. In this regard, in the knowledge base, two types of driving factors are taken into account to determine the forecasting model: long-term trend-related features such as macro-economic factors and short-term factors such as temperature. The knowledge base is decomposed into three-step-related parts to deal with these groups' substantial differences in frequency. Firstly, the long-term trends are identified by the tendency variables. Then, the short-term variability is considered by temperature-related variables in addition to day type. Machine learning regression methods, support vector machines, random forest, and artificial neural networks are compared to determine the non-linear relationship of variables in these steps. The results of these steps are combined in the third step. Then, in the inference engine, a new reasoning method based on fuzzy logic is represented to forecast the regional long-term hourly load under uncertain evidence of temperature as an effective non-predictable feature in the long run. To evaluate the performance of the proposed system, a comparison against five systems is conducted. The results show the superiority of this system compared to the other systems for a publicly available dataset (ISO New England electricity market) as well as Iran’s residential, commercial and agricultural electricity load.

Economic efficiency of alternative border carbon adjustment schemes: A case study of California Carbon Pricing and the Western North American power market

To be effective, policies addressing power sector carbon emissions in jurisdictions that are embedded in larger carbon markets must account for carbon embodied in imports and the potential for leakage. Yet analyses of alternative border carbon adjustment (BCA) policies that carefully model the impact of generation mix, net load distributions, and transmission limits are rare in the literature. With a generation-transmission expansion planning model of Western North America, we compare the impacts of alternative BCA schemes that California's carbon pricing system could implement. These include no BCA, facility (import source)-based deemed emission rate, a facility-neutral and constant (over-time) rate, and a facility-neutral and dynamic rate. Our results suggest that, when compared to no BCA or BCA using facility-based deemed rates, a facility-neutral BCA can improve efficiency by simultaneously lowering market-wide emissions and costs without raising California consumer expenditures. Emissions leakage also declines significantly. The precise value of the deemed rate affects these gains; setting it equal to marginal emission rates external to California is most efficient. We also show that California's carbon pricing encourages more interstate transmission expansion because power imports become more profitable. However, BCAs that cost-effectively lower total regional emissions will dampen those incentives.

Information exchange based clustered differential evolution for constrained generation-transmission expansion planning

Abstract Proper investments for expansion of generation, transmission and distribution systems in an electric grid is a very important issue that rely on optimal expansion planning of the grid resources. Investments on transmission network influence those in generation and distribution side which motivates a co-optimization of all these different resources of a grid. The co-optimization based Generation - Transmission Expansion planning is a large scale, constrained, hard bound optimization problem. This research article proposes an Information exchange based Clustered Differential Evolution algorithm (IE-CDE) for solving the problem of expansion planning of generation and transmission resources in an electric grid. The proposed algorithm is first tested extensively on the CEC 2017 constrained optimization benchmark problems and the results are compared with those obtained by state-of-the art algorithms to investigate the efficiency of the proposed algorithm in solving challenging constrained optimization problems. Then the proposed algorithm IE-CDE is used to solve the challenging Generation-Transmission expansion planning problem (GT) on a test system called Garver system. The implementation is also extended to incorporate the expansion planning of demand management resources along with generation and transmission resources (GTD) on the same test system mentioned before as well as an additional one called IEEE 24 bus system. The results obtained by proposed IE-CDE on the GT and GTD expansion planning problems are compared with state of the art algorithms in the literature and the comparison reveal that the proposed method is able to find better solutions than the other algorithms yielding lower cost of expansion for the electric grid. The claim for superiority of the proposed method over others is also substantiated by statistical significance tests on the obtained results.

Dynamic Market-Based Generation-Transmission Expansion Planning Considering Fixed Series Compensation Allocation

This paper presents a market-based multi-period generation-transmission expansion planning (GTEP) along with fixed series compensation (FSC) allocation. FSCs can dispatch power more efficiently over the transmission network as well as trading opportunities for market participants and thus improve market surplus and reduce the total transmission investment. The proposed planning may accordingly enhance network efficiency and improve social welfare for all participants. The proposed model is structured as a mixed integer linear programming (MILP) problem. The CPLEX solver, as a commercial solver, is used to solve this MILP problem. Moreover, to find a reliable and viable optimal topology, N − 1 security criterion is employed through the proposed model. This criterion is used to take into account any unanticipated operating condition due to unexpected transmission line failures. The proposed model is applied to the Garver and IEEE 24-bus systems as well-known systems to show the effectiveness of FSC in dynamic GTEP.

A bi-level integrated generation-transmission planning model incorporating the impacts of demand response by operation simulation

If all the resources in power supply side, transmission part, and power demand side are considered together, the optimal expansion scheme from the perspective of the whole system can be achieved. In this paper, generation expansion planning and transmission expansion planning are combined into one model. Moreover, the effects of demand response in reducing peak load are taken into account in the planning model, which can cut back the generation expansion capacity and transmission expansion capacity. Existing approaches to considering demand response for planning tend to overestimate the impacts of demand response on peak load reduction. These approaches usually focus on power reduction at the moment of peak load without considering the situations in which load demand at another moment may unexpectedly become the new peak load due to demand response. These situations are analyzed in this paper. Accordingly, a novel approach to incorporating demand response in a planning model is proposed. A modified unit commitment model with demand response is utilized. The planning model is thereby a bi-level model with interactions between generation-transmission expansion planning and operation simulation to reflect the actual effects of demand response and find the reasonably optimal planning result.

An agent-based simulation approach to congestion management for the Colombian electricity market

Power markets are complex systems in which decisions made by independent actors must satisfy technological constraints while providing a reliable service that increases social welfare. While agents freely trade their energy, trades must satisfy physical constraints of the transmission grid. The mechanisms for organizing trade and operating networks are increasingly complex and there is a growing need for computational tools that help understand how market rules work under transmission constraints. We present a hybrid optimization and agent based simulation approach for analyzing changes in transmission pricing in Colombia. The model has a module for transmission based on optimal flow problems and a market module that simulates the market operation. We show how the approach proposed is useful for evaluating the performance of generation–transmission expansion plans.

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

To address the planning issue of offshore oilfield power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm (GTHA) is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously, which outweighs the disadvantages of separate planning, for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently. Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.

A Multiyear Security Constrained Hybrid Generation-Transmission Expansion Planning Algorithm Including Fuel Supply Costs

This paper addresses the problem of a multiyear security constrained hybrid generation-transmission expansion planning. It is assumed that the overall generation requirements of a network are known along the planning horizon, but their allocations are unknown. Moreover the fuel cost throughout the network is not uniform. By allocating the overall generation capacity among the grid nodes, and determining the new transmission element additions along the planning horizon, the overall cost of the system is minimized. The problem is formulated as a mixed integer nonlinear programming problem, which for a large-scale system is very difficult to solve. In this paper a new constructive heuristic approach is proposed, so that the problem can be readily solved. To assess the capabilities of the proposed approach, two networks are studied: the Garver test grid as a small grid and the Iranian power grid as a large-scale grid.