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British Columbia (BC) is committed to transitioning to a low-carbon energy system to meet its CO2 emission reduction targets, but this shift towards renewable energy sources may have significant implications for land use. This paper investigates the land-use impacts of different electrification pathways and technology choices in BC's energy system using the BC Nexus model. Our analysis highlights the potential increase in land-use requirements associated with transitioning from fossil fuels to renewable energy sources, with the occupied land of the power system potentially increasing up to six times larger than the current total build-up land (depending on the scale of electrification and technology choice). These findings have important implications for policymakers in terms of balancing the trade-offs between energy security, economic development, and environmental sustainability. By understanding the physical footprint of the energy transition, decision-makers can develop more effective climate policies and sustainable development strategies.

Multi-day energy storage (MDS), a subset of long-duration energy storage, may become a critical technology for the decarbonization of the power sector, as current commercially available Lithium-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models (CEMs)), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale CEM. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: (1) modeling MDS with linked representative periods is crucial to capturing its full value, (2) MDS value is highly sensitive to the cost and availability of other resources, and (3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle electrification scenario in the U.S. from 2020–2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ∼300 GW more solar PV) and activity (up to ∼460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g. day, night, uncontrolled) but also how these evolve over time.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

The European Union aims to deploy a high share of renewable energy sources in Europe's power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply–demand balance. Green hydrogen (GH) can increase power systems' flexibility and decrease renewable energy production's curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around €30/MWh. • Green hydrogen (GH) increases the flexibility of the European power systems. • Scenarios with GH in the power system were compared to a no-hydrogen scenario. • By 2050, the deployment of 140 GW of GH might pay off investments. • The economic viability of GH for flexibility use depends on its participation in other hydrogen markets. • The break-even point for GH is estimated at €30/MWh, approximately. [ABSTRACT FROM AUTHOR]

Summary: The design and operation of cost‐effective power systems and high penetration of renewable energy and energy storage require a modeling approach that simultaneously reflects the system's reality. Although power systems with high penetration of renewable energy and energy storage have been analyzed in the past, more systematic analysis are in need to determine the impact of generation units' techno‐economic parameters on the model's accuracy. This study investigated the effects of the techno‐economic and spatial‐temporal parameters, which will be referred to as combined techno‐economic parameters, via seven scenarios. The combined techno‐economic effects were demonstrated on optimal generation mix, renewable curtailments, variability status of storage units, and energy cost. The omission of combined techno‐economic parameters results in the renewable generation to be more visible and with less curtailment while significantly decreasing the share of energy storage and decreasing the energy cost by 1.53 USD/MWh. As a result, if power systems assessment dismissed the evolving techno‐economic and spatial–temporal parameters, the precision of cost–benefit analysis can be compromised, thus affecting the decision‐making in terms of directing projects, regulations, and policies within the power industry. [ABSTRACT FROM AUTHOR]

We consider capacity addition decisions by a new product manufacturer faced with uncertain technology alternatives. The manufacturing capacity addition and technology development occurs in parallel, with preliminary results from a technology project's success providing valuable information to the manufacturer in adding capacity. We solve a stochastic dynamic program with Bayesian updates to obtain the manufacturer's expected profit‐maximizing capacity investment decision. Our model and applications are motivated by the Critical Materials Institute (CMI) (funded by the Department of Energy), which manages research projects focused on mitigating critical material constraints, vital to renewable energy technologies such as direct‐drive wind turbines, electric vehicles, and energy‐efficient lighting. We capture three unique aspects of the problem: first, the learning from project progress depends on task‐based stochastic outcomes and a project's percent‐done. Second, the underlying technology's profitability is based on a model of competition. Third, we evaluate the impact of progress across a portfolio of projects based on a manufacturer's capacity addition. We develop a heuristic that produces results that are close to optimal and can thus be used for large problem sizes. The managerial insights from an application of our model to CMI projects include: (i) technology projects that report the percent‐done of a project earlier increase expected manufacturer profit; (ii) careful choice of "safe bets," that is, technologies with low profitability but a high probability of success, can increase expected manufacturer profit; (iii) a portfolio of projects can increase profits significantly over separate project evaluation; and (iv) dynamic management of project resources can increase overall profit. [ABSTRACT FROM AUTHOR]

Simulating energy systems is vital for energy planning to understand the effects of fluctuating renewable energy sources and integration of multiple energy sectors. Capacity expansion is a powerful tool for energy analysts and consists of simulating energy systems with the option of investing in new energy sources. In this paper, we apply clustering based aggregation techniques from the literature to very different real-life sector coupled energy systems. The purpose is to provide a robust comparison of methods to complement the literature, in which methods are either not compared or compared on very similar energy systems. We systematically compare the aggregation techniques with respect to solution quality and simulation time. Furthermore, we propose two new clustering approaches with promising results. We show that the aggregation techniques result in solution time savings between 75% and 90% with generally very good solution quality. To the best of our knowledge, we are the first to analyze and conclude that a weighted representation of clusters is beneficial. Furthermore, to the best of our knowledge, we are the first to recommend a clustering technique with good performance across very different energy systems: the k-means with Euclidean distance measure, clustering days and with weighted selection, where the median, maximum and minimum elements from clusters are selected. A deeper analysis of the results reveals that the aggregation techniques excel when the investment decisions correlate well with the overall behavior of the energy system. We propose future research directions to remedy when this is not the case. Finally, we believe that to further strengthen the research area, a library of benchmarks instances should be developed. [ABSTRACT FROM AUTHOR]

Increased adoption of distributed energy resources in the form of renewable energy sources (RESs) to handle the capacity expansion in the power distribution system (PDS) is a new paradigm. RESs being intermittent in nature, present operational issues that worsen with increasing RES penetration rate. An attractive approach for handling the intermittency is coordinated reactive power control (RPC) of the RESs in the PDS. RPC leverages on the reactive power capacity of the RES inverters and hence is a significantly cheaper option than the conventional solutions such as energy storage systems. Oversizing of inverters is considered to improve the robustness of RPC, however, malicious attackers may exploit the oversized capacity to strengthen their attacks. In this study, a demonstration of data integrity attacks launched against RPC and its impact on the availability of the power supply at the prosumer premises are presented. Validation for the attack using simulations and emulated experiments in a scaled‐down power system test‐bed is also presented. The analytical, simulation and experimental results illustrate the feasibility of tripping the circuit breaker at the prosumer site and causing power supply interruption. Finally, a defense mechanism is proposed to minimise the impacts and it is validated using MATLAB–SIMULINK simulations. [ABSTRACT FROM AUTHOR]

Decarbonization of the electricity sector is one of the major measures in slowing down the pace of climate change. In this paper, we analyze the impacts of energy storage systems (ESS) and year-to-year variability and uncertainty in the hourly profiles of variable renewable energy (VRE) on power system decarbonization in 2050. We perform this analysis through capacity expansion optimization based on technology cost projections and CO 2 emission restrictions based on 11 years of wind, solar, and load data variations in Italy's power system, with a particular focus on how ESS changes the optimal generation portfolio and system performance. We also explore the impact of ESS duration on the renewables' adoption and system costs. To quantify the impact of VRE variability in different years, we present a comparative analysis of capacity expansion optimization based on multiple-year and single-year data. Our results indicate a high RES penetration even in the absence of decarbonization policies, due to expected declines in future technology costs. In the transition to a zero-carbon electricity system, carbon capture and storage (CCS) with less than 100% carbon capture efficiency will play a role only in intermediate low-emission scenarios unless future technology advancements improve its capture efficiency. ESS investments contribute to lower total system costs by replacing more expensive flexibility resources. However, the value of ESS changes by its duration, with longer ESS durations having lower marginal value per added kWh storage capacity. Variability in VRE profiles leads to substantial variation in the system's configuration and energy cost, depending on what year is used in the optimization. Decision making based on single-year data can therefore lead to substantial increases in the systems' operational costs in other years due to increased probability of capacity shortages and load curtailments. In contrast, optimizing over multiple VRE and load years provides a more robust and cost-effective generation expansion strategy. • Results show high RES penetration levels even without decarbonization policies. • Decarbonization is less expensive with ESS, under given cost assumptions. • The marginal value of EES drops rapidly as more capacity is added. • Annual variations in VRE significantly change the system configuration and costs. • Planning with single-year data can lead to high operational costs in other years. [ABSTRACT FROM AUTHOR]

The rapid growth of electricity demand in Iraq has consistently outstripped the country's electricity infrastructure, leading to frequent blackouts, especially during peak summer demand. With a heavy reliance on oil 93 % and gas 7 % for electricity generation, the nation is exposed to economic fluctuations associated with global oil prices and environmental impacts from burning fossil fuels. This study explores the implications of integrating smart grids and expanding renewable distributed generation capacity in the country's energy system. Drawing upon the projection of the energy demand by 2035 across four scenarios (Business as Usual, Sustainable Development, High Growth, and Low Carbon), the study underscores solar energy, backed by abundant solar and wind energy, as a key player in the country's renewable transition. However, challenges persist, including the need for technical expertise in renewable energy technologies, financing hurdles, and concerns about policy stability. With the country's policies targeting 25 % renewable electricity by 2030 and the establishment of significant renewable projects, there tangible momentum. The results have drawn out a roadmap for an estimated addition of up to 10 GW of renewable energy capacity by 2030 and 14 GW by 2035. In addition, strategies for distributed generation capacity expansion were highlighted, underscoring the crucial role of solar and wind energy and other renewables in the country's sustainable energy future. • Addressing a frequent blackout caused by electricity demand surpassing infrastructure, particularly during summer peaks. • Targets 25 % renewable electricity by 2030, focusing on solar and wind amid economic and environmental concerns for Iraq. • The abundant solar and wind resources play a central role in the country renewable energy transition. • Potential renewable capacity addition could reach to 10 GW by 2030, increasing to 14 GW by 2035. [ABSTRACT FROM AUTHOR]

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs. [ABSTRACT FROM AUTHOR]

Providing enough generation capacity is a critical issue in power systems, which depends on the amount of invested resources by the firms. Restructuring of the electrical power systems has changed the return on invested costs in a reasonable time into the foremost factor in generation expansion. This study applies the system dynamics technique for analyzing the generation expansion strategies in electricity market. Moreover, the effect of different factors such as interest rate, demand elasticity, and load growth rate are also considered. In this regard, a simulation study of the generation expansion model is performed with a follow-up period of 33 years. Two different reliability indices are taken into account as indicators for generation investment, namely, reserve ratio and reserve variation; while the first one is highly used in the literature the latter one is proposed in this study. The reserve variation approach results in lower price deviations, lower magnitude of price spikes, and lower lost load in the uniform price-based energy market, analyzed in this study. In addition, the effect of extending the wind turbines as renewable resources on generation capacity expansion is investigated. The obtained results demonstrate that integration of the wind turbines damp the severe price fluctuations and gradually takes the place of gas turbines. [ABSTRACT FROM AUTHOR]

Cogeneration of water and power in integrated cogeneration production plants is a common practice in the Middle East and North Africa (MENA) countries. There are several combinations of water desalination and power technologies which give significant adverse environmental impact. Renewable and alternative energy technologies have been recently proposed as alternative power production paths in the water and power sector. In this study, we examine the optimal capacity expansion of water and power infrastructure over an extended planning horizon. A generic mixed integer linear programming model is developed to assist in the decision making process on: (1) optimal installation of cogeneration expansion capacities; (2) optimal installation of renewable and alternative power plants; (3) optimal operation of the integrated water and power supply chain over large geographical areas. Furthermore, the model considers the installation of carbon capture methods in fossil-based power plants. A case study will be presented to illustrate the mathematical programming application for the Emirate of Abu Dhabi (AD) in the United Arab Emirates (UAE). The case study is solved reflecting different scenarios: base case scenario, integration of renewable and alternative technologies scenario, and CO 2 reduction targets scenario. The results show that increased carbon tax values up to 150 $/ton-CO 2 gives a maximum 3% cost increase for the supply chain net present value. The installation of carbon capture methods is not an economical solution due to its high operation energy requirements in the order of 370 kW h per ton of captured CO 2 . Thus, the Cplex solver in GAMS chooses optimal solutions without installation of carbon capture processes. In addition, higher degree of alternative and renewable energy technologies penetration within the energy mix reduces the overall net present value of the network, and the carbon emissions by 40%, and 12%, respectively. [ABSTRACT FROM AUTHOR]

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids. • The number of consecutive days for energy arbitrage changes the operation of storage. • The optimization model selects longer-duration storage when the modeled time horizon is increased. • Seasonal energy storage is deployed when R&D efforts reduce CapEx to $1.3/kWh. • Long duration energy storage will decrease overbuilding renewable energy by up to 10%. • We use a capacity expansion model with high resolution for Western North America. [ABSTRACT FROM AUTHOR]

• Day-ahead and balancing markets are modelled in stochastic rolling horizon dispatch • Stochastic rolling horizon dispatch is integrated with a capacity expansion problem • Benders cuts are formulated from a stochastic rolling horizon dispatch model • The impact of short-term uncertainty on investments are investigated Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and balancing electricity markets within a capacity expansion model. We use Benders cuts and a stochastic rolling horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs. [ABSTRACT FROM AUTHOR]

Abstract: The capacity expansion planning is a crucial process taking into account multiple aspects and various parameters of the examined power sector in order to optimally satisfy the future electricity demand. In the case of Greece, the long-term energy planning of the electricity supply sector faces a lot of challenges deriving from the peculiarities and geomorphology of the country mainly referring to the large number of islands. This paper explores the feasibility and the consequences of interconnecting Greek islands to the mainland grid by establishing two alternative scenarios of least cost electricity planning for the period 2009–2020. Namely, the reference scenario assumes isolated electric systems of islands and is compared against the alternative option of their interconnection to the mainland. The main purpose of the present study is to illustrate the great importance of islands’ interconnection on the development of renewable energy sources (RES) to generate electricity while pointing out the consequent economic and environmental benefits. [Copyright &y& Elsevier]

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors' forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms' heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm's own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent. [Display omitted] • A new agent-based model linking electricity sector investments and markets. • Agents are risk-averse, adaptive, heterogeneous firms with imperfect information. • Results compared against traditional perfect competition capacity expansion. • Model captures realistic suboptimalities, capacity scarcities and recourse actions. • Outcomes depend not only on firm's own parameters but also on other firms' actions. [ABSTRACT FROM AUTHOR]

• A robust unit commitment model is proposed for hybrid AC/DC transmission grids. • Complete decomposition of network feasibility evaluation improves scalability. • Nonanticipativity is preserved via robust generator-wise reserve margins. • Improved Efficiency, computation time, and robustness for large-scale grids. • Improved loadability and hosting capacity for renewable energy sources. The day-ahead energy and reserve management considering transmission restrictions and voltage security limits is a challenging task for large-scale power systems in the presence of volatile renewable energy sources. In this paper, a two-stage robust optimization model is proposed for the joint scheduling of energy and reserve for hybrid AC/DC transmission grids. The optimal schedule, including the commitment, power dispatch, and reserve allocations is computed subject to the feasibility of nonlinear AC and DC network constraints under a specified set of scenarios. Furthermore, the proposed decomposition approach renders the second stage (network security evaluations) into standard optimal power flow problems, which addresses the scalability issues observed with existing methods. A second-order cone relaxation is applied to nonlinear AC and DC network constraints to improve the computational tractability. In simulations, the proposed robust optimization model is illustrated using a recently proposed hybrid AC/DC transmission grid architecture with a specific topology. The results demonstrate the effective utilization of the grid capacity to accommodate more demand and renewable energy sources. This hybrid AC/DC transmission grid architecture induces exactness of the convex relaxation, supporting the validity of the optimal day-ahead schedule. The efficiency and computational performance of the proposed method is compared to the existing literature and the robustness is verified using an out-of-sample analysis. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative operation strategy to extend the acceptance of EVC (Electric Vehicle Charger) and RES (Renewable Energy Resource) in LVDS (Low Voltage Distribution System) by introducing an ESS (Energy Storage System). In conventional LVDS, the load and RES capacity are designed not to exceed the pole transformer capacity. However, when the ESS is connected to the end of LVDS and the bidirectional power flow becomes possible, the linkable capacity of the load and renewable energy can be improved up to twice the capacity of the pole transformer. In addition, even though the power consumption of the load and the power generation of RES exceed the pole transformer capacity, it is possible to maintain the feeder capacity and grid voltage within the allowable limit by the appropriate operation of the ESS. The simulations are performed in the environment of PSCAD/EMTDC, and the ability of the proposed strategy is assessed and discussed. [ABSTRACT FROM AUTHOR]