Transmission Capacity Expansion Research Articles

Emerging environmental justice issues at the intersection of transportation and electricity systems

Rapid decarbonization of the transportation system is needed to address global climate change, and electrification of the transportation sector will likely be an important strategy to achieve decarbonization goals. While electrification is an effective approach to reducing carbon emissions, it may result in environmental justice consequences that need to be tackled. We discuss four categories of related issues: air quality and health-related equity; technology access; equitable infrastructure development; and a just global supply chain. In regions where grid decarbonization is well under way, transport-related disparities in air quality and health are expected to decrease with electrification. In contrast, in regions that still rely heavily on coal generation, disparities may increase, putting further strain on low-income communities and people of color. The high upfront cost of electric vehicles and limited access to charging present additional challenges for achieving equity in transportation electrification. Meeting the electricity demand of a fully electric vehicle fleet will require rapid expansion of power generation, transmission, and distribution capacity, and the location and design of this infrastructure will have further impacts on communities where it is sited. Here, we offer a perspective on these emerging environmental justice issues at the intersection of transportation and electricity systems and provide policy recommendations and future research directions for electrified transportation. We suggest there is a need for policies targeting electrification and power sector decarbonization in tandem, assessment of barriers to electric vehicle adoption in different groups, development of strategies for community inclusion in infrastructure development decisions, and creation of frameworks to assess equity tradeoffs along the global supply chain supporting electric vehicles and renewable energy technologies.

A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

Achieving Emission-Reduction Goals: Multi-Period Power-System Expansion Under Short-Term Operational Uncertainty

Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment in generation and transmission capacity. We build on this setting by considering a multi-year investment horizon for finding the optimal plan for generation and transmission capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours in power-system operations to capture hydropower operations and flexibility requirements for utilizing variable renewable-energy sources such as wind and solar power. To improve the computational performance of existing exact methods for this problem, we employ Benders decomposition and solve a mixed-integer quadratic programming problem to avoid computationally expensive big-M linearizations. The results for a realistic case study for the Nordic and Baltic region indicate which investments in transmission, wind power, and flexible generation capacity are required for reducing greenhouse gas emissions. Through out-of-sample experiments, we show that the stochastic adaptive robust model leads to lower expected costs than a stochastic programming model under increasingly stringent environmental considerations.

Integration of wave energy into Energy Systems: an insight to the system dynamics and ways forward

Wave energy is a rich and highly accessible renewable energy resource, that has largely been under-developed. Studies from the sector have tried to show the potential of benefits wave energy in “simple cases” or via small hybrid systems, the large scale incorporation of wave energy has not yet been fully investigated. Our approach uses a fully dynamic climate driven energy system model, which has undergone modifications to include wave energy converters and their associated dependencies.
 This study explores the system dynamics and important elements that will be used for large scale wave energy integration; in a fully coupled European Energy System. We explore the cost pathways of different wave energy converters, the impact of climate data, and the impact of transmission capacity expansion under cost-optimal configurations of a multi-renewable European power system. From this preliminary approach we aim to provide the boundary conditions, and assumptions that will govern the integration of wave energy into the European Energy System up to 2050.

Multi-objective optimization design of a grid-connected hybrid hydro-photovoltaic system considering power transmission capacity

The first step in creating a hybrid system that incorporates the adjustable hydropower station is selecting an appropriate scale of new energy. This study proposed a methodology for optimized design an on-grid hydro-photovoltaic (PV) system considering power transmission capacity. The concepts of short-term joint hydro-PV operation were first given. The on-grid hybrid hydro-PV system's two evaluation indicators were then proposed, integrating double carbon targets and new energy power curtailments. Then, an optimization design model was established with maximum carbon emission reductions and minimum PV power curtailments. Finally, the methodologies were applied to a case study in northwest China to test its rationality. Results manifested that (1) PV power curtailments increase with carbon emission reductions under the given PV size and runoffs, and the optimized PV size was 200 MW; (2) the utilization rate of the newly add transmission channel generally decreases gradually with an increase in power transmission capacity expansion, and the optimum power transmission capacity expansion was 10 MW with the maximum utilization rate of 12.36%; (3) the hydro-PV joint production was impacted by both the characteristics of PV output and runoff. Thus, this study helps to the joint development of flexible hydropower stations and volatile new energy sources.

실시간 동적 송전용량 산정 시스템을 위한 가공 송전선 감시 시스템 개발

As NDC(Nationally Determined Contributions) was raised in the Paris Agreement in 2015, power generation using renewable energy that can minimize carbon use has become a more important task. However if a new renewable power generation source is unilaterally connected to the grid, the transmission capacity increases than the current capacity and due to the nature of new and renewable energy, the fluctuation range of power generation is larger which can increase system instability. Therefore efficient transmission line operation is required. Moreover it is difficult to actually implement the expansion of transmission capacity because social acceptance is not good. Therefore in order to solve this problem real-time dynamic transmission capacity (Forecasted Dynamic Line Rating) calculation technology that can maximize the use of transmission capacity in existing lines is required. This paper describes the development of a system that can monitor the surrounding environment of transmission towers and thermal rating data of overhead lines in real time to implement the calculation algorithm.

Developing a Robust Expansion Planning Approach for Transmission Networks and Privately-Owned Renewable Sources

Power system restructuring has changed transmission expansion planning (TEP) and caused many complications due to conflicting and contradictory objectives. The transmission capacity expansion would significantly affect the revenue of investor-owned renewable energy sources (RESs). Thus, the investment decisions on merchant RESs must be considered in the TEP studies conducted by the transmission system operator (TSO). In this regard, this paper aims to propose a multi-objective co-planning of investment in transmission networks and merchant RESs with three objective functions: minimizing the investment cost of newly deployed transmission lines, minimizing transmission congestion cost, and minimizing load curtailment in N-1 conditions. Moreover, the TSO guarantees a desirable rate of return for private investors to finance renewable energy projects. Further, a robust optimization (RO) technique is employed to cope with the uncertainties associated with demand and renewable energy production. Also, a posteriori multi-objective optimization algorithm, i.e., the non-dominated sorting genetic algorithm (NSGAII), is applied to solve the advanced optimization problem, followed by the fuzzy min-max method to acquire the final optimal solution. Finally, the IEEE RTS 24-bus test system is utilized to demonstrate the effectiveness and applicability of the suggested approach.

Principal spatiotemporal mismatch and electricity price patterns in a highly decarbonized networked European power system

SummaryAs the European power system decarbonizes, the variability of the mismatch between renewable generation and demand, as well as that of electricity prices, are expected to increase substantially. Because mismatch and prices show complex temporal and spatial interaction, we propose the use of principal component analysis (PCA) to investigate them. We unveil their main spatiotemporal patterns, examine their cross-correlation, and their dependence on the transmission capacity expansion and emissions reduction in a highly renewable cost-optimal electricity model. We find that the majority of variance in both the mismatch and price time series is explained by just three principal components (PCs). Hence, a convenient switch of basis vectors allows expressing the time series as combinations of few components which are shown to have intuitively interpretable structures. Moreover, we find that the temporal coherence between the first three PCs of mismatch and prices are substantially reinforced as the system decarbonizes.

Impacts of power sector model features on optimal capacity expansion: A comparative study

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. Many powerful tools exist to find optimal capacity expansion. In a stylized comparison of six models, we evaluate the capacity expansion results of basic power sector technologies. The technologies under investigation include base- and peak load power plants, electricity storage, and transmission. We define four highly simplified and harmonized test cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all test cases. Fixed energy-to-power ratios of storage can entirely change optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and final-period storage levels can affect the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure substantially deviate from linear optimization models, as missing foresight and limited flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions, and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

Capacity expansion of chaotic secure transmission system based on coherent optical detection and space division multiplexing over multi-core fiber.

We propose and experimentally study a coherent optical chaotic secure transmission system through a multi-core fiber (MCF). The messages are encrypted by the chaotic carrier and transmitted through the outer cores of the MCF, whereas the chaotic carrier signal is concealed by transmitting through the center core. The MCF provides large transmission capacity expansion and security enhancement against eavesdroppers due to its physical structure. In addition, the designed optical chaos self-homodyne coherent detection strategy has high detection sensitivity and simple physical structure. Due to the prevalence of devices and digital signal processing (DSP) algorithms used in this system, it can be well compatible with a commercial coherent optical communication system. Error free 40 Gb/s/core encrypted 16 quadrature amplitude modulation (QAM) signal transmission over 10 km 7-core fiber is achieved, and 20 Gb/s quadrature phase shift keying (QPSK) signal transmission over a 100 km standard single-mode fiber (SSMF) is demonstrated to verify the long-distance transmission capability. The sensitivity to the secret key is also studied.

Energy trading efficiency in ERCOT’s day-ahead and real-time electricity markets

The efficient market hypothesis (EMH) is a fundamental tenet of active and unfettered market trading. The EMH for intertemporal trading of a commodity such as natural gas implies that today’s futures price is an unbiased predictor of a future delivery period’s spot price and that the difference between today’s futures and spot prices reflects the commodity’s cost of carrying. The EMH for inter-regional trading requires the commodity’s regional price difference to equal the inter-regional transportation cost. Using regional hourly data for January 1, 2011 to December 31, 2020 from the Electric Reliability Council of Texas (ERCOT), we investigate whether the EMH is empirically valid in all ERCOT’s wholesale electricity markets and, if not, the extent of ERCOT’s energy trading inefficiency and what can be done to reduce it. By estimating a parsimonious system of eight price-level regressions and four price-difference regressions, we reject the interday EMH for all regions and the inter-regional EMH for all regional market pairs. However, ERCOT’s extent of trading inefficiency is mild when compared to the wholesale energy prices. Our empirical examples have two policy implications: enhancing ERCOT’s interday trading efficiency entails improving the day-ahead forecasts for solar and wind generation and refining ERCOT’s indicative real-time market prices, while enhancing ERCOT’s inter-regional trading efficiency requires transmission capacity expansion to reduce transmission congestion and line losses.

Merchant transmission in single-price electricity markets with cost-based redispatch

Transmission expansion is a complex problem in energy market design and research has not yet provided a market-based solution that is superior to a (partly) regulated approach. Furthermore, markets with a single market clearing price lack regional incentives for system friendly generation or transmission capacity expansion. In this paper, we propose a market design for transmission expansion that can be implemented in single-price markets with cost-based redispatch and we describe its properties. We show that our market solution is incentive compatible, satisfies the ’beneficiary pays’ requirement and leads to a welfare optimal grid expansion otherwise achieved by an integrated optimization approach of a benevolent grid operator. We apply the mechanism to the German electricity system in 2018, 2019 and 2030 as an example and show that transmission capacity expansion is greatly reduced using the mechanism instead of a no-congestion regulation. We also test the robustness of the approach to erroneous generation capacity expectations and find that the impact on economic results is limited. Finally, we extend our approach to include congestion reducing generation capacity investment and discuss the strategic effects on a 6-node reference grid.

Impact of power-to-gas on the cost and design of the future low-carbon urban energy system

Power-to-gas technology has been proposed as one component for future energy systems facing decarbonization targets. This paper presents a power-to-gas focused open optimization model for studying cost efficient design and operation of future urban energy system. The model is able to distinguish the benefits of different configurations of power-to-gas by modelling several energy vectors, including electricity, heating, and cooling alongside with different plant components. The usefulness of the built multi-vector model is illustrated by a case study where the benefits of power-to-gas are studied in the context of a medium-sized Nordic city. The results show that the city is able to reach carbon neutrality with the help of power-to-gas. Power-to-gas provides cost savings by reducing the need of heat storages and transmission capacity. The savings are greatest when the emission reduction goal is high and transmission capacity expansion is expensive. Direct air capture appears as the superior carbon dioxide source when compared to post combustion capture from flue gases due to costs and annual availability. The case study shows no economic benefit for distributed power-to-gas.

Energy trading efficiency in the US Midcontinent electricity markets

Efficient trading under wholesale market competition reduces an electric grid’s energy costs for meeting time- and location-dependent demands. We analyse energy trading efficiency by estimating three newly developed sets of energy price difference regressions interconnected by their common root of energy price level regressions. Using a sample of ~ 0.6 million hourly observations for 01/01/2014 to 10/31/2020, our estimation documents energy trading efficiency of the 10 local resource zones across 15 American states administered by the Midcontinent Independent System Operator (MISO). We find MISO’s zonal energy markets integrated across space (zone j vs. zone k for j ≠ k) and time (day-ahead market vs. real-time market). However, these markets exhibit inter-day energy prices differences that move with the fundamental drivers (e.g., day-ahead forecasts for natural gas price and zonal wind generation) of day-ahead energy prices and their forecast errors. Inter-zonal day-ahead energy price differences move with the fundamental drivers and their zonal variations. Inter-zonal real-time energy price differences increase with inter-zonal day-ahead energy price differences and depend on zonal variations of the fundamental drivers’ forecast errors. Our empirics yield two important policy implications. First, enhancing inter-day trading efficiency requires accuracy improvements in (a) day-ahead forecasts for natural gas price, zonal load levels, and zonal wind generation, and (b) day-ahead scheduling of zonal nuclear and must-run generation. Second, improving inter-zonal trading efficiency requires mitigating inter-zonal transmission congestion via transmission capacity expansion, generation investment and demand reduction in a load pocket, and virtual bidding for inter-zonal energy price differences.

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Welfare-maximizing transmission capacity expansion under uncertainty.

We apply the JuDGE optimization package to a multistage stochastic leader-follower model that determines a transmission capacity expansion plan to maximize expected social welfare of consumers and producers who act as Cournot oligopolists in each time period. The problem is formulated as a large-scale mixed integer programme and applied to a 5-bus instance over scenario trees of varying size. The computational effort required by JuDGE is compared with solving the deterministic equivalent mixed integer programme using a state-of-the-art integer programming package. This article is part of the theme issue 'The mathematics of energy systems'.

100% Renewable Energy Scenarios for North America—Spatial Distribution and Network Constraints

The urgency to combat climate change and the widely distributed, increasingly competitive renewable resources in North America are strong arguments to explore scenarios for a renewable energy supply in the region. While the current power system of North America is heavily dependent on fossil fuels, namely natural gas, coal and oil, and some nuclear power plants, some current policies at the state level, and future federal policies are likely to push the share of different renewable sources available in Mexico, the U.S., and Canada. This paper explores three scenarios for a renewable energy supply, using a bottom-up energy system model with a high level of spatial and time granularity. The scenarios span the extremes with respect to connecting infrastructure: while one scenario only looks at state-level supply and demand, without interconnections, the other extreme scenario allows cross-continental network investments. The model results indicate that the North American continent (a) has sufficient renewable potential to satisfy its energy demand with renewables, independent of the underlying grid assumption, (b) solar generation dominates the generation mix as the least-cost option under given renewable resource availability and (c) simultaneous planning of generation and transmission capacity expansion does not result in high grid investments, but the necessary flexibility to integrate intermittent renewable generation is rather provided by the existing grid in combination with short-term and seasonal storages.

Minimising the effects of spatial scale reduction on power system models

The spatial resolution of energy system optimisation models (ESOMs) is often compromised for computational performance. Reducing the spatial resolution impacts the least-cost solutions when optimising the generation capacity and transmission capacity of the ESOMs with high penetration of variable renewable energy sources. Previous studies show that the two main effects of spatial resolution reduction are the increase in solar and wind expansion capacity and the decrease in transmission capacity expansion. This paper introduces a targeted method of defining regions during spatial scale reduction by using the max-p-regions clustering algorithm to aggregate similar areas into regions. The attributes used to determine the similarity between areas in the max-p-regions method are population; wind and solar resource potential; and pumped-hydro storage capacity. Two alternative spatial resolution reduction methods were used to compare how the effects of spatial resolution impacted the optimisation results of the ESOMs. Evaluation results for three country groups showed that using the max-p-regions method to define regions caused the two effects of spatial resolution reduction to be generally lower than using national jurisdictions. For the case studies Germany and Spain, the results showed that the max-p-regions method identified sets of regions, which were less impacted by the two effects of spatial resolution reduction.

Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors

In this study, we successfully develop the transmission planning problem of large-scale power systems based on generalized shift-factors. These distribution factors produce a reduced solution space which does not need the voltage bus angles to model new transmission investments. The introduced formulation copes with the stochastic generation and transmission capacity expansion planning problem modeling the operational problem using a 24-hourly load behaviour. Results show that this formulation achieves an important reduction of decision variables and constraints in comparison with the classical disjunctive transmission planning methodology known as the Big M formulation without sacrificing optimality. We test both the introduced and the Big M formulations to find out convergence and time performance using a commercial solver. Finally, several test power systems and extensive computational experiments are conducted to assess the capacity planning methodology. Solving deterministic and stochastic problems, we demonstrate a prominent reduction in the solver simulation time especially with large-scale power systems.