Transmission Investment Research Articles

Delivering equity in low-carbon multisector infrastructure planning

Many countries worldwide are transitioning from fossil fuel-dependent economies to carbon neutrality, driven by the 2030 agenda for sustainable development and the Paris Agreement. However, without considering the regional distribution of essential services like water and energy, this transition could inadvertently maintain or increase inequities, threatening sustainable development. Here, we argue that spatial equity of benefits should be considered in planning low-carbon energy transitions, especially in developing countries with multisector interdependencies and high service disparities between regions. We propose an analytical framework that can help analysts and policymakers plan for regionally equitable climate-compatible futures. The multisector design framework combines integrated river basin-power system simulation with artificial intelligence design tools. The utility of the framework is demonstrated for Ghana by identifying the most efficient infrastructure intervention portfolios and their implied trade-offs between spatial equity in water and energy service provision, carbon emissions, food production, and river ecosystem performance. Case-study results show that an equitable low-carbon energy transition will require increased investments in renewable energy and transmission alongside more informed infrastructure system planning. With low renewable investments, equity can be improved, but at the cost of higher emissions and electricity supply curtailments.

Optimization Approach for Hydrogen Infrastructure Planning Under Uncertainty.

Toward the Net-Zero goal, deciphering trade-offs in strategic decisions for the role of hydrogen is vital for transitioning to low-carbon energy systems. This work proposes a two-stage stochastic optimization framework to provide insights for infrastructure investments in hydrogen production, storage, transmission, and CO2 capture and storage. The mixed-integer linear programming (MILP) model aims to minimize total system cost with detailed spatiotemporal resolution to meet hydrogen demand in Great Britain. Uncertainty is considered in hydrogen demand, gas, and technology costs, as well as renewables and biomass availability. To address the resulting combinatorial complexity, scenarios are reduced using forward scenario reduction. Optimization results indicate that a combination of autothermal reforming and biomass gasification with carbon capture and storage (CCS) is the most cost-efficient strategy under uncertainty. A what-if analysis explores the impact of water electrolysis penetration on the production mix. The results demonstrate that considering uncertainties provides a risk-averse strategy for decision-making in low-carbon hydrogen pathways.

Transmission and Generation Expansion Planning Considering Virtual Power Lines/Plants, Distributed Energy Injection and Demand Response Flexibility from TSO-DSO Interface

This article presents a computational model for transmission and generation expansion planning considering the impact of virtual power lines, which consists of the investment in energy storage in the transmission system as well as being able to determine the reduction and postponement of investments in transmission lines. The flexibility from the TSO-DSO interconnection is also modeled, analyzing its impact on system expansion investments. Flexibility is provided to the AC power flow transmission network model by distribution systems connected at the transmission system nodes. The transmission system flexibility requirements are provided by expansion planning performed by the connected DSOs. The objective of the model is to minimize the overall cost of system operation and investments in transmission, generation and flexibility requirements. A data-driven distributionally robust optimization-DDDRO approach is proposed to consider uncertainties of demand and variable renewable energy generation. The column and constraint generation algorithm and duality-free decomposition method are adopted. Case studies using a Garver 6-node system and the IEEE RTS-GMLC were carried out to validate the model and evaluate the values and impacts of local flexibility on transmission system expansion. The results obtained demonstrate a reduction in total costs, an improvement in the efficient use of the transmission system and an improvement in the locational marginal price indicator of the transmission system.

Optimal Grid Expansion Planning in Power Systems With Surplus Generation Capacity and Suppressed Demand

ABSTRACTDeveloping countries often grapple with uncoordinated planning of generation and transmission, leading to excess generation capacity and suppressed demand due to limited power delivery from inadequate transmission infrastructure. This study aims to address this challenge by proposing a coordinated generation and transmission expansion planning (GTEP) model that optimises investments to utilise surplus generation and meet suppressed demand. We develop a multistage stochastic GTEP model and compare its performance against isolated generation expansion planning (GEP) and transmission expansion planning (TEP) models on the IEEE 24‐bus reliability test system (RTS) using PLEXOS software. Results show that the GTEP model recommends 14.29% less generation capacity expansion (1500 MW) compared to the GEP model (1750 MW) over the planning horizon, while prioritising transmission reinforcements. This translates to a significant reduction in surplus generation (dump energy) of 38.17%. The TEP achieves a 42.94% reduction in dump energy, but this is offset by a higher risk of supply shortages due to a lack of consideration for future generation capacity expansion. Our findings demonstrate that the combined GTEP model offers an optimal expansion strategy, effectively utilising existing generation capacity and unlocking suppressed demand through strategic investments in both transmission and generation.

Electricity security in Uganda: Measurement and policy priorities

Reliable, accessible, affordable, and sustainable electricity systems are fundamental to modern and progressive economies. For developing societies, particularly those with fragile power generation capabilities and electricity networks, the electricity security metrics are incomparable and remain untested. A Multi-Criteria Decision-Making (MCDM) method that follows an entropy-based weighting system is employed to quantitatively evaluate and construct an Electricity Security Index (ESI) for Uganda. Results reveal that Uganda is “moderately” electricity secure. Out of the maximum possible score of 100, a year-on-year analysis indicates that the ESI score was highest in 2007 (56) and the lowest was registered in 2015 (39). Based on the VIKOR technique, it is observed that electricity reliability contributes the most to the ESI, while electricity access contributes the least. Although expansion of electricity generation capacity is necessary, energy policies should, in equal measure, prioritize adequate investments in robust and modern transmission and distribution infrastructure. A riveting policy implication is that governance quality enhances a country's electricity system by reinforcing the other dimensions of electricity security.

An Extra-High Voltage Test System for Transmission Expansion Planning Studies Considering Single Contingency Conditions

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically.

Production or transmission investments? A comparative analysis

The European electricity market is moving towards an integration process, and to facilitate that integration and the flow of electricity in that market, the European Transmission System Operator proposes different investments in transmission and production capacities. I develop a model of imperfect competition to study those investments, and I identify the economic forces that determine the equilibrium price and the flow of electricity: The size and the competitive effects. I prove formally when those forces operate simultaneously or independently, and how they determine consumers’ surplus and suppliers’ profits. I prove that an investment in transmission capacity outperforms an investment in production capacity by maximizing consumers’ welfare, but the performance in terms of suppliers’ profits is ambiguous.

EFEKTIVITAS PENGGUNAAN ABSEN FINGER PRINT UNTUK MENINGKATKAN DISIPLIN KARYAWAN DI FAKULTAS EKONOMI DAN BISNIS UNIVERSITAS MULIA BALIKPAPAN

The use of fingerprint timesheets makes it easier for the Faculty of Business Economics (FEB) at Mulia University to monitor employee discipline regarding work entry and leaving times, and makes it easier to record employee attendance data online. Employees whose attendance exceeds 8.15 am are considered late, and will receive sanctions such as a deduction from the employee's transportation allowance. Even though there is already the use of fingerprint absences and sanctions for being late, there are still employees who show up late. This research aims to provide information regarding the effectiveness of implementing fingerprint absences at FEB Mulia University. This research uses descriptive qualitative research to provide an overview based on facts about the object under study. Data collection was carried out by interviewing various sources and observing the use of fingerprint absences at FEB. The results of this research provide information that the use of finger print attendance has been effective for FEB, because it reduces the use of paper (paperless), takes faster time to retrieve data from the finger print machine to Excel, reduces the risk of incorrect data recaps and increases employee attendance discipline. The use of fingerprint absences also experiences obstacles such as difficulty detecting fingerprints, resulting in repeated absences, susceptible to didisease transmission and expensive investment costs.

Co-optimizing transmission and BESS expansions with system strength constraints

Co-optimizing transmission and BESS expansions with system strength constraints

Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices

Europe is transitioning towards a sustainable energy economy. Planning these future power and energy systems is multifaceted and complex, requiring long-term projections of future electricity market dynamics. The prognostication of future prices is crucial for power system planning, as these prices will drive investments and impact incentives to build and improve new systems. The price of electricity in different regions impacts transmission investment. Energy systems models have high-dimensional output and are expensive to evaluate, leading to unique challenges in managing the uncertainty in model assumptions. This study presents a novel methodology for determining which cost assumptions are most impactful on the output of a detailed model of the European energy system. Because this model is computationally intensive, only a small number of samples are available to us. A multiple-output support vector regression is used as a surrogate of predicted electricity prices timeseries. We find that solar and wind costs are the primary drivers of electricity prices, but natural gas costs generally drive peak electricity prices and those far in the future.

Merchant investment in electricity transmission networks

Many electricity economists have long hoped that it might be possible to establish competition, not just in providing generation and load assets, but also in the provision of the transmission network itself. Attempts in the 1990s to achieve so-called merchant transmission investment using conventional Financial Transmission Rights (FTRs) proved disappointing. We develop an extension of screening-curve models to include the optimal choice of both generation and transmission. Such models, although stylised, provide valuable insight into the long-run co-optimisation of generation and transmission. We propose new regulatory and merchant transmission investment mechanisms that achieve the socially optimal investment.

Limited impact of within-vector ecology on the evolution of malaria parasite transmission investment

Malaria parasites spend part of their life in a vertebrate host and the rest in an arthropod vector and must successfully navigate both environments to gain fitness. In vertebrate hosts, malaria parasites infect red blood cells and can either replicate asexually or develop into the sexual form required for transmission to the vector. Despite the clear fitness benefits of onward transmission, only a small proportion of malaria parasites convert to sexual development. Mathematical models seeking to test the plausibility of various hypotheses to explain these low “conversion rates” have focused almost exclusively on the vertebrate/host half of the parasite life cycle. Here, we examined how processes occurring in the vector, including density-dependent parasite development and parasite-induced vector mortality, influence the evolution of parasite conversion rate in the host by developing a multi-scale model of within-host infection dynamics and parasite within-vector developmental processes for rodent malaria. We found that, regardless of model specifications (e.g., definitions of fitness, magnitude of parasite-induced vector mortality), considering processes within the vector had only a weak influence on the optimal conversion rate, but substantially diminished the fitness returns for all strategies and resulted in a sharper declines off the optima. Our approach allowed us to derive new metrics of parasite fitness (which we call “infectivity functions”) that link within-host gametocyte density to the probability of transmission to new hosts after passing through the vector, and that prevent overestimation of parasite transmission potential.

Transmission Investment Coordination Using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this paper introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies.

Large scale integration of wind power – influence of geographical allocation

This paper investigates the influence of geographical allocation of wind power generation in Northern Europe, assuming large scale integration of wind power. The work applies a linear cost optimization model of the heat and power sector with a 1-hour time resolution. The model minimizes the sum of running costs to meet the heat and power demand and the wind power and transmission investment costs. Wind data are taken from modelled wind speed data from the Swedish Meteorological and Hydrological Institute. The Nordic countries and Germany were divided into regions and the 200 sites with the highest yearly output were chosen to represent the region. The model gives the most favourable distribution of wind power between the regions. In addition, the paper provides an assessment of the effect of geographical distribution of wind power with respect to influence on the aggregated wind power production (only considering the wind power generation itself). The modelling results show that the largest investments in wind power are made in the windy region of Southern Norway. However, depending on the cost of transmission allocating wind power near large load centers in Germany may also be favourable. As for the assessment of distribution of wind power, the wind data gives that if the 400 best sites in Europe were used, this would result in a capacity factor of 38.5% and a lowest output of 2.5 % of rated power (applying 2009 wind data).

Interrelationships of renewable energy zones in Queensland: localised effects on capacity value and congestion

Renewable Energy Zones (REZ) are in the early stages of implementation in Australia's National Electricity Market (NEM) as a means of expediting transmission investment and facilitating new renewable generation capacity. This research explores the interrelationships and seasonal behaviour of nine proposed REZ across the Queensland region of the NEM. Assessment of the seasonal variable renewable energy supply and localised congestion effects, highlights the rapid increase in spilled energy that occurs as REZ subscription matures. Further, the contribution to the reliable supply of energy at a system level is considered through capacity value analysis and contextualised with their recent market performance. Important system planning considerations are highlighted, where analytical techniques are applied to the resource adequacy and congestion management problem.

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.

Stochastic Transmission Planning: Latent Factor Approximation of AC Costs With DC Subproblems

Transmission planning requires solving an optimal power flow problem for each considered investment plan for multiple operating conditions. AC power flow formulations are more accurate, but for transmission planning on large systems may be computationally intractable and can converge to local optima. Regional transmission studies usually rely on linearized DC power flow formulations, which reduce computation time but sometimes produce solutions that are inferior when the AC flows are resolved. We propose an approach to regional-scale transmission planning that approximates the AC power flow results using the faster DC formulation. An initial sample of investment plans is simulated with both AC and DC power flows, and then the singular value decomposition is applied to characterize the error between the two formulations and form a projection that estimates the AC costs from the DC costs. The estimation is used in a DC formulation of the transmission investment problem. We show that the proposed approach accurately estimates the AC operation costs and returns expansion plans that are consistently reliable with AC power flows. We also demonstrate that traditional DC formulations can produce low-cost, AC-feasible investment plans if enough representative hours are selected and weighted effectively.

Chance-constrained coordinated generation and transmission expansion planning considering demand response and high penetration of renewable energy

Chance-constrained coordinated generation and transmission expansion planning considering demand response and high penetration of renewable energy

Grid-scale demand-side flexibility services using commercial buildings lighting loads

Grid-scale demand-side flexibility services using commercial buildings lighting loads

Monetary transmission and government investment in China

Monetary transmission and government investment in China