Embedding flexibility in transmission expansion planning: A real-options in projects approach

This paper addresses stochastic transmission expansion planning (TEP) under uncertain load conditions when reliability is taken into consideration. The main objective of the proposed TEP is to minimize the total planning cost by denoting the place, number, and type of new transmission lines subject to safe operation criteria. In this paper, the objective function consists of two terms, namely, investment cost (IC) of new lines and reliability cost. The reliability cost is incorporated as the loss of load cost (LOLC). Network uncertainties in the form of loads are molded as Gaussian probability distribution function (PDF). Monte-Carlo simulation is applied to tackle the uncertainties. The proposed stochastic TEP is expressed as constrained optimization planning and solved using shuffled frog leaping algorithm (SFLA) SFLA is compared to other optimization techniques such as particle swarm optimization (PSO) and genetic algorithms (GA). Finally, stochastic planning (planning including uncertainty) and deterministic planning (planning excluding uncertainty) are compared to demonstrate impacts of uncertainty on the results. Simulation results in different cases and scenarios verify the effectiveness and viability of the proposed stochastic TEP, including uncertainty and reliability.

Worldwide there is an increasing need for a more sustainable form of electrical power delivery with a growing share of renewable energy generation. In the distribution and transmission network, large-scale and small-scale wind and solar power plants will be introduced, in proportion to the annual economic growth. The transmission and distribution network will be expanded, focusing on the electricity transport, however, there will also be a need for exchanges with neighboring countries. Alternative solutions are needed in order to support the changes of the future grid. High temperature superconductors are an alternative to conventional conductors, due to their high current density and very low AC loss, and therefore deserve more attention. The purpose of this study is to explore ways to integrate high-temperature superconducting cables in a future network and to compare their favorable technical properties with, e.g., the conventional XLPE cable. The development of 2nd generation high temperature superconducting tapes results in a high tape quality, making it very attractive for use in superconducting power transmission cables. At the same time, the network requirements placed on the grid, based on society needs are changing, such as low magnetic field emissions, reducing space requirements, lower losses, minimizing visual intrusion, etc. Our study shows that superconducting cables compared to conventional cables score better on these societal requirements. From our comparison of three practical low and high temperature superconductors we can conclude that Yttrium Barium Copper Oxide is the most suitable superconductor for use in transmission cables. Our techno-economic analysis shows that superconducting cables become already competitive with conventional cable in the AC transmission, such as XLPE cables. Possible future problems concerning the transport capacity in the power grid where high temperature superconducting cables can offer a solution have been identified. For one promising location, we have formulated the requirements for the design of a high-temperature superconducting cable. Next, we propose two types of cable systems (with cold and warm dielectric). For both types we describe the core, the electrical insulation, the screen, the cryostat, the cooling system, etc. Also for the distribution grid a techno-economic investigation is conducted. To assess the feasibility of the application in distribution grids, we have experimentally demonstrated a reduction of AC conductor losses from 1 W/m to 0.1 W/m. We also carried out an experimental investigation to improve the developed cryostat design for a 6 km long cable connection. Despite these substantial technical improvements our economic study showed that the high-temperature superconducting distribution cable is not yet competitive with the present conventional distribution cable systems except for niche locations where additional advantages e.g. magnetic emission, reduced space usage, power density weighs more heavily. Besides the HVAC grid the Netherlands has HVDC interconnections with neighboring countries and there are initiatives for the use of DC high voltage connections to wind farms further out to sea. In our study, we make reference to a suitable location, where the above mentioned attractive features of the superconducting cable are applicable as well. We advised a modified design of a HTS HVDC cable which enables a possible upgrade of the transmission capacity of the HVDC link at such location. The main results from the investigation are that: • Based on our techno-economic analysis HTS cables offer the most competitive solution in transmission grids. Introduction of such cables will reduce HTS tape price, which in turn will stimulate further applications. • Conceptual designs of competitive HTS AC and DC transmission cables are formulated along with that for HTS AC distribution cable. Novel designs allow for much longer length between cooling stations. • Our experimental research has shown that HTS cable core losses may be reduced by a factor 10 (down to 0.11 W/m/phase at 3 kArms, 77 K, 50 Hz). • Dedicated low friction cable cryostat was developed and successfully tested for 47 meters length. Patented multi-layer thermal insulation improves the cable cryostat heat leak from 1 W/m to 0.1 W/m.

Transmission expansion planning (TEP) is critical for the power grid to meet fast growing demand in the future. Traditional TEP model does not utilize the flexibility in the transmission network that is considered as static assets. However, as the load profile may have different seasonal patterns, the optimal network configuration could be very different for different seasons in the planning horizon. Therefore, this paper proposes to incorporate seasonal network optimization (SNO) into the traditional TEP model. SNO dynamically optimizes the network for each season of each planning epoch. Two TEP-SNO models are proposed to investigate the benefits of optimizing the status of (i) existing branches, and (ii) existing and new branches, respectively. Numerical simulations demonstrate the effectiveness of the proposed TEP-SNO models. It is shown that SNO can improve system operational efficiency, defer investment of new transmission elements, and reduce the total cost.

The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

This paper presents a market-based multi-period transmission expansion planning 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 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 lines failures. The proposed model is applied to the Garver system as well-known system to show the effectiveness of FSC in dynamic transmission expansion planning (TEP).

With increasing penetration of renewable energy sources and electric vehicles (EVs) in power grids, developing appropriate models for power system planning is of great importance. This article proposes a novel gray wolf optimization (GWO) algorithm-based transmission expansion planning (TEP) method considering renewable energy sources and EVs and evaluates the performance of alternating current power flow (ACPF) and direct current power flow (DCPF) models in the TEP problem in terms of accuracy, run-time, and objective functions. The uncertainty associated with renewable energy sources, EVs, and loads are explicitly modeled in this study using probability density functions (pdfs) and the Copula approach. The proposed GWO-based TEP method is validated by case studies using the IEEE 24-bus reliability test system (RTS) and IEEE 118-bus test system. The ACPF model leads to fewer transmission lines and lower costs than the DCPF model in most scenarios.

<p>Power grid is an interconnected system of supplying electricity from the supplier to the consumer, consisting of electricity generating plant, high voltage transmission lines- to carry electricity from the generating plant to the load center, and distribution lines- to carry electricity from load centers to individual consumers. A lot of research is being pursued to develop technologies for improving the next generation of power grid called the Smart Power Grid. The Smart Power Grid will have sophisticated communication infrastructure to improve the efficiency of electricity generation using renewable energy sources like the sun, water, etc and also to inform consumers of their electricity usage pattern. Also, the electricity market is now divided into three sections- generation, transmission and distribution. Private companies are competing with each other to provide electricity at the most competitive market price. We have developed two algorithms to help generating companies achieve their goal of meeting the hourly electricity need of the consumers and to do so at a minimum total cost.</p>

This paper proposes a genetic algorithm (GA) to solve the transmission system expansion planning (TSEP) problem in power systems. The transmission network is represented using the DC power flow model. The problem is then formulated as a mixed integer nonlinear problem (MINLP) which is very complex to solve in large-scale networks using classical optimization algorithms. Genetic algorithms (GAs) are a robust metaheuristic tool which can deal efficiently with optimization problems, like the TSEP problem. The most important features and improvements of the developed GA are presented. Test results are obtained for two test systems to show the good performance of the algorithm.KeywordsTransmission Expansion PlanningGenetic Algorithms

Construction times of new power plants are subject to uncertainty in deregulated electricity markets and they have an important impact on the planning of the expansion of the transmission network. In this paper, we develop a model of transmission expansion planning that is based on Robust optimization and considers the uncertainty in the instant when new power plants are connected into the electricity grid. Using this transmission expansion planning model, we assess the impact of postponing the connection time of some new power plants over the system cost and the optimal network expansion plan. By obtaining the optimal solutions of the proposed model for different levels of robustness, we are able to rank the new power plants with respect to how much the delay of each project negatively affects the system cost. We illustrate our methodology and the performance of two solution approaches proposed to solve our model by using a 34-node stylized version of the main Chilean grid. In the case of the main Chilean grid, the results obtained indicate that some of the projects whose delays negatively affect the most to the system cost are the renewable power plants projected for the next ten years. This is important from the regulator viewpoint because it allows the assessment of renewable energy policies that accelerate the installation of renewable new power plants also in terms of their impact over the transmission expansion planning.

This paper presents a multistage transmission expansion planning (TEP) which alleviates the level of transmission congestion during the planning horizon. Due to the combinatorial nature of the proposed TEP model, a Benders decomposition approach intertwined with a manipulated disjunctive model is applied to decompose the original TEP problem into a master problem and two sub-problems standing for system security and optimal operation. The security sub-problem applying the N-1 contingency criterion to assess the transmission system security. Besides, the optimal operation sub-problem makes it possible for the proposed TEP model to fulfill the optimal operation as well as to calculate the difference between system lacational marginal prices (LMPs) for all buses. Also, this model appends a specific term into the TEP objective function as the congestion level (CL) reflecting the degree of market competitiveness. Therefore, the proposed TEP model minimizes the total costs comprising the investment cost of candidate transmission lines as well as the level of transmission congestion. The proposed approach is applied to the northeastern area of the Iranian power grid.

Electric power has become an essential part of daily life: we plug our electronic devices in, switch our lights on, and expect to have power. As the availability of power is usually taken for granted in modern societies, we mostly feel annoyed at its absence and perceive the importance of power during outages which have severe effects on the public order. Blackouts have had disastrous consequences for many countries and they continue to occur frequently. Such examples demonstrate the necessity for careful analysis and planning of power grids, to ultimately increase the reliability of power grids. The power grids have evolved due to economic, environmental and human-caused factors. In addition to the contingency analysis, nowadays, the operation and planning of power grids are facing many other challenges (such as demand growth, targeted attacks, cascading failures, and renewable energy integration). Thus, many questions arise, including: which buses (nodes) to connect with a new line (link)? What are the impacts of malicious attacks on power grids? How may an initial failure result in a cascade of failures? How to prepare for the integration of renewable energy? Answering such questions requires developing new concepts and tools for analysing and planning of power grids. Power grids are one of the largest and the most complex man-made systems on earth. The complex nature of power grids and its underlying structure make it possible to analyse power grids relying on network science. The applications of network science on power grids have shown the promising potential to capture the interdependencies between components and to understand the collective emergent behaviour of complex power grids. This thesis is motivated by the increasing need of reliable power grids and the merits of network science on the investigation of power grids. In this context, relying on network science, we model and analyse the power grid and its near-future challenges in terms of line removals/additions, malicious attacks, cascading failures, and renewable integration.

This paper studies transmission expansion planning (TEP) problem considering effects of wind farms on transmission network congestion, power system reliability, line repair, and operational costs of wind and generating units in a deregulated environment. Transmission congestion is modeled using difference between local marginal prices (LMPs) of start and end buses of each line that is multiplied by its power flow. In this way, wind units can efficiently affect network congestion through decrease in lines loading and LMPs of buses. Transmission system reliability is modeled using load shedding (LS) index as well as outage rate and outage duration of a single line or simultaneous outage of a line and a wind unit. Also, generation reliability is formulated using loss of load, value of lost load, and LS criteria due to outage rates and outage durations of generating and wind units considering line limits. In addition, the effect of wind units on repair cost of transmission lines is modeled by the relationship among wind generation, line failure rates and lines loading. Operational cost includes cost of network losses, pollution penalty, fuel expenses, and generation cost of wind units. In order to more efficiently investigate the effect of wind power on TEP problem, the proposed model was tested on the IEEE Reliability Test System (IEEE RTS) for different scenarios, and the results were discussed.

Wind capacity growth in the Northwest United States: Cooptimized versus sequential generation and transmission planning

A topological investigation of phase transitions of cascading failures in power grids

In the 130 years since the invention of the legacy electric power system concept, electrical has been adjusted to match electrical consumption (i.e., the load) as it varies throughout the time of day and seasons. This follows paradigm is a major roadblock to the large-scale incorporation of renewable energy into the national power grid since energy sources such as wind and solar provide inconsistent, variable power that cannot easily be controlled to follow consumption. As a result, today's centrally planned and controlled power system design is no longer adequate. This paper is to introduce a new control approach to enable a follows generation paradigm where a flexible engineered system with distributed control at the users' sites will revolutionize the power industry. Customers will be able to generate power on-site, purchase power from a variety of sources (including each other), sell power back to the grid, select the level of the supply reliability they wish to purchase, and choose how to manage their electricity use. The resulting solution will make the electricity grid significantly more efficient and robust by facilitating extensive use of renewable energy sources and reducing end-use losses in the system. With renewable energy sources widely distributed (e.g., roof-top solar panels and wind farms), the proposed approach will allow exchange of power among utilities, market service providers, consumers, and aggregators (services representing many loads), and also allow power exchange within a customer site. By incorporating this flexibility into their operations, utilities will be able to adjust the overall load they must serve to match available power. This follows generation approach is essential to allow unhindered inclusion of low-emission renewables in the electricity grid. At the same time it makes consumers active agents in the energy exchange. Earlier research addressed discrete aspects of the scientific challenges, but the new approach called Flexible Load Energy eXchange (FLEX) will provide the system-level, holistic approach to achieve its vision. The overarching goal is to develop the science, technology, and control system design required to enable energy exchange between the customer and other parties in the electricity energy exchange ecosystem to unlock the huge potential of renewable generation. A central impediment is the stochastic variability of renewable energy sources such as wind and solar, prompting some to question if dependence on renewable sources will ever be viable. Even when wind (which tends to produce at night) and solar (produced during the day) are combined, severe variations in require huge adjustments (termed ramping within the industry), and spinning (on-line) reserves using conventional generation. This paper explores the missing critical component of smart grid development: smart and flexible loads. In addition, power interruptions, which are often caused by generation/load imbalances or faults on end-user radial connections, will be greatly reduced in a FLEX-enabled power grid by on-site or alternate customer generation. FLEX will also enable customer participation to create new market arrangements, such as wholesale and retail options, and incentives to increase energy efficiency not previously available.