A Hierarchical Collaborative Optimization Model for Generation and Transmission Expansion Planning of Cross-Regional Power Systems Considering Energy Storage and Load Transfer

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

Power systems around the world are changing due to the rapid transformation of the generation mix. Unprecedented configurations of the power system may increase the exposure to failures, and thus, lower security of supply. At the same time, the secure development of the system cannot neglect cost considerations. This demands for adequate tools to assess and prepare for future scenarios of the energy transition. In this paper, a risk-informed approach for generation and transmission expansion planning is developed. The approach integrates cost-based generation and transmission expansion planning and risk-based transmission expansion planning. Interfacing these two models to perform coordinated generation and transmission expansion planning results in solutions that are cost-effective, account for the risk implications of the systemic changes, and guarantee system security. The risk-informed coordinated generation and transmission system expansion planning is performed for the Swiss power system which is modeled in full detail for the year 2050. To account for electricity exports and imports, the four surrounding countries are considered in an aggregated manner and their 2050 capacities are projected assuming a net-zero scenario. The results of the risk-informed coordinated expansion planning show that the pure cost-minimization approach applied to either generation or combined generation and transmission expansion planning does not necessarily lead to a reliable power supply in Switzerland. In fact, not accounting for reliability leads to expansion solutions that may incur up to 16 times higher demand not served compared to the reference year 2018. This highlights the importance of considering in-depth system security analyses in power system expansion planning.

An emission-constrained approach to power system expansion planning

Concerning the integration of large-scale wind power, an integrated model of generation and transmission expansion planning is proposed based on the assessment of the value of steady state and dynamic security. In the assessment of security value, the unit commitment simulation based on the predicted hourly load and wind power output data in the planning horizon is used to evaluate the costs of preventive control, emergency control and social losses due to the uncertainty of load and wind power. The cost of preventive control consists of the fuel cost of power generation, the environmental cost and the load shedding cost. This not only provides a systematic method of security assessment of power system expansion planning schemes, but also broadens the perspective of power system planning from the technology and economic assessment to the measure of the whole social value. In the assessment process, the preventive control and emergency control of cascading failures are also presented, which provides a convincing tool for cascading failure analysis of planning schemes and makes the security assessment more comprehensive and reasonable. The proposed model and method have been demonstrated by the assessment of two power system planning schemes on the New England 10-genarator 39-bus System. The importance of considering the value of security and simulating hourly system operation for the planning horizon, in expansion planning of power system with integration of large-scale wind power, has been confirmed.

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

Normally generation expansion planning and transmission expansion planning are carried out separately, which will lead to non-matching between the generation system and the transmission system, and asset idle. This paper proposes an integrated multi-scenario generation and transmission expansion planning model in order to combine the generation expansion planning and transmission expansion planning together. In the integrated generation and transmission expansion planning (IGTEP) model, DC power flow constraints for alternative and existing branches are formulated for linearization separately.The uncertainties of the power system increase with the increased integration of intermittent wind power generation. The uncertainties of the load and wind power are handled with scenarios analysis method. In this paper, the K-means clustering method is selected. Three different cases are analyzed including 1) the annual maximum load without considering the wind power; 2) the timing load and wind power; 3) the timing load and wind power considering the priority of wind power integration.The IGTEP model is a mixed-integer linear programming problem, which can be solved with commercial optimization solver. Finally, the Garver-6 bus system is selected for the new model verification on MATLAB platform. Results demonstrate the mathematical characteristics and power system practical meanings of the models and methods the paper proposed.

Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.

Generation and transmission expansion planning with high penetration of wind farms considering spatial distribution of wind speed

Experience with the transmission expansion planning of a longitudinal power system is reported. To reach an optimal choice among several feasible expansion plans, indices such as maximum power transfer limits, fault currents, transient stability, system losses, and cost, which have been widely used by utilities, are computed to compare the relative merits of these alternatives. In view of past experience with low-frequency oscillations taking place in the study system, oscillatory stability of the power system under each expansion plan is examined in detail by means of eigenvalue-eigenvector analyses. Recommendations are made with regard to future expansion plans on the basis of the present study.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In general, power system expansion planning can be classified into three types such as generation expansion planning (GEP), transmission expansion planning (TEP) and distribution system expansion planning (DEP). Several researches have been performed in GEP and TEP. But, a huge research gap is still available in the topic of DEP. DEP can be classified into two optimization problems, namely substation expansion planning (SEP) and optimal feeder routing. This study attempts to solve real-time SEP problem for the power sector of Tamil Nadu, a state in India. Due to several design variables, the grouping of discrete and continuous variables frames the SEP problem as complicated. Hence, a promising optimization algorithm is essential to solve this complicated problem. The application of differential evolution (DE) algorithm has been presented to obtain the results with the sizes and sites of both the existing and proposed substations (SS) by satisfying the subjected constraints.KeywordsSubstation expansion planningDifferential evolutionTamil NaduLeast cost

A clear gap exists between the way that power system expansion planning is modeled in academic research papers and the real-world practice of system planners. To address this gap, we perform two comparative numerical case studies on a large-scale ERCOT-like test system. In the first study, we quantify the economic benefit of optimization-based TEP over the approach of project-by-project screening of expansion plans that is more popular in industry. The second study compares the solutions of resource-only expansion planning with those of joint resource and transmission expansion planning considering four future scenarios representing uncertainties that exist in emission reduction policies and forecasted demand. Numerical case studies illustrate the significant economic and technical benefits of co-optimization in resource and transmission expansion planning.

Impact of forecast errors on expansion planning of power systems with a renewables target

Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem.

Network Expansion Planning (NEP) plays a pivotal role in the development of power systems. It involves investing in new generating units and transmission lines to meet growing load demands and ensure a reliable electricity supply. Historically, the incorporation of demand response (DR) factors in power system planning has been limited due to their complexity and evaluation challenges. However, with advancements in smart grid technologies, increased integration of renewable energy, and the emergence of flexible loads, the inclusion of DR models has become crucial for enhancing power system reliability. While numerous studies have delved into generation and transmission expansion planning (GTEP) problems, only a few have explored the integration of network payment schemes and DR within the GTEP framework. This study proposes a multi-annual generation and transmission expansion planning model that incorporates three network payment schemes and two DR techniques. The objective is to secure financing for new generating units and transmission lines while minimizing the overall system cost. The proposed models employ the mixed-integer linear programming (MILP) optimization method and are validated using a modified IEEE 24-bus system. Two key system performance metrics, namely the network congestion index and network saturation index, are employed to assess system reliability and effectiveness. These results demonstrate that the integration of network payment schemes and DR techniques into the generation and transmission expansion planning model can lead to a cost reduction of 32.1% as compared to base model, reduced power system congestion and saturation (22.1%, 2.73%) to allow more renewable energy integration and enhanced power system reliability and operational flexibility.

This study proposes a model based on mixed-integer linear programming (MILP) for the integrated expansion planning of generation and transmission systems with the implementation of distributed generation (DG). Most DG planning takes place after generation and transmission planning has been conducted. This model can be used to include DG potential simultaneously with generation and transmission expansion. DG is modelled as a negative load therefore DG is treated as a non-dispatchable unit of power generation. The objective of the model is to minimize overall cost including the investment cost of the generation units, DG units, and transmission lines, and the operating cost of the generation and DG units. The proposed model is static-deterministic model in the form of MILP. The model was evaluated using the 6-bus Garver’s test. To prove the effectiveness of the model, it was evaluated using the IEEE 46 Bus Test. The results show that due to the impact of DG on power system expansion planning ,the overall cost was reduced. The simulation results also show that a different optimal network configuration can be achieved by DG implementation in expansion planning.