Unserved Energy Research Articles

Optimal energy storage configuration to support 100 % renewable energy for Indonesia

This study presents a renewable energy (RE) optimization study to model the pathway to achieve 100 % carbon abatement, focussing on options for storage, using Indonesia's national electricity grid as a case study. Utilizing the PLEXOS energy simulation tool, the study covers the period 2021–2045. It employs an optimization of cost minimization function approach, encompassing investment, operation, maintenance, and unserved energy. The study integrates various components, including electricity supply and demand, transmission, renewable sources, and energy storage, while considering operational, build, and renewable energy target constraints. A range of scenarios are explored, varying in RE targets, battery capacities, and whether to include open-cycle gas turbines. The key novelty of this study is considering multiple versions of battery storage, with different options for the number of hours of storage. The findings indicate that higher RE targets lead to increased total installed nameplate capacity, with a significant portion from battery storage. In the early phases, batteries with 2-hour of capacity prioritized for short-term needs. As the focus shifts to more extended targets, batteries with a 4-hour capacity are recognized as more cost-effective and become the predominant choice. Over time, the least-cost strategy evolves to incorporate 10-hour capacity batteries to meet long-term energy storage requirements. To achieve a 100 % RE target by 2045, it is estimated that alongside every 100 MW of wind and solar capacity, there should be a corresponding 42 MW of energy storage. However, interpretations of these findings must consider the limited temporal resolution, uncertainties in demand and cost, and challenges related to grid inertia from energy storage, which may affect the stability and feasibility of the proposed solutions. This research offers crucial insights for energy policy and infrastructure development in renewable energy and storage system implementation.

Taking a Portfolio approach to wind and solar deployment: The case of the National Electricity Market in Australia

We formulate a new, computationally inexpensive framework based on Mean–Variance Portfolio Theory to determine the optimal allocation of wind and solar capacity, which can leverage the complementarity between intermittent solar and wind generation to meet electricity demand at the lowest cost, with gas acting as reserve generation in times of insufficient wind and solar output, and the variance of the mismatch between demand and the output of wind and solar is taken as the risk indicator. We then apply this framework to the context of the National Electricity Market in Australia. Our result reveals that, assuming sufficient network capacity, the estimated lowest generation cost achievable is $45.26/MWh, where the expected percentage of unserved energy (USE) is 0.0044%. For a risk-averse planner, a level of USE less than 0.0001% may be achieved at the expense of almost doubling the cost to $88.23/MWh. Under the current network capacity constraints, we estimate these costs to increase to $53.09/MWh and $89.90/MWh respectively, while the USE could rise to 0.017%. Moreover, network expansions in Queensland are found to be the most beneficial. On the other hand, a 4-hour battery storage system with power capacity equivalent to 20% of the peak load could help reduce the cost by up to 6.8% alongside further decrease in the USE. Our results are also shown to differ drastically from the conventional, output-based approach. Since the two approaches address two different questions depending on whether demand is flexible, demand flexibility is also an important factor.

The Cost of Unserved Energy: Evidence from Selected Industrial Cities of Pakistan

The Cost of Unserved Energy: Evidence from Selected Industrial Cities of Pakistan

Unified dispatch of grid-connected and islanded microgrids

This work develops microgrid dispatch algorithms with a unified approach to model predictive control (MPC) to (a) operate in grid-connected mode to minimize total operational cost, (b) operate in islanded mode to maximize resilience during a utility outage, and (c) utilize weighting factors in the grid-connected objective function to preserve islanded capability (on-site fuel reserves, battery state of charge) to enhance resilience in the potential event of an unplanned grid outage. Resilience is defined using microgrid survivability (probability to serve 100% of critical load), autonomy (duration of time to serve 100% of critical load), and unserved energy (curtailed critical load) for a target of 7 days during a grid outage. The developed methods are applied to a military microgrid with 2,250 kW of diesel generation, 3,450 kW/13,800 kWh battery storage, and 16,479 kW of solar photovoltaics. Sensitivity analysis is conducted to determine the selection of weighting factors to have the best impact on three developed objectives: grid-connected economics, islanded resilience, and carbon intensity. Optimal weighting factors reduce operating costs by 0.1%, increase survivability by 3.9%, increase autonomy by 16.7%, reduce unserved energy by 94.1%, and increase carbon intensity by 2.5%.

Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets

As the proportion of variable renewable generation capacity in our electricity systems rises, so too will the associated absolute forecast errors in their generation. This work quantifies the complex interactions between short-term renewable generation forecasts, short-term demand forecasts, generator unit commitment and economic dispatch in electricity systems with levels of variable renewable generation spanning 49–79% of annual electricity demand. When the short-term operational uncertainty caused by renewable generation becomes larger in absolute terms than that of demand, the accuracy of renewable generation forecasting has a critical influence on electricity market performance. That is, inaccurate short-term wind forecasts can increase hours of unserved energy and scarcity hours spent at market price cap, increase total system costs and prices, significantly increase returns to thermal generators and energy storage, reduce returns to wind generators, and increase greenhouse gas emissions slightly. For the generalised case study considered in this work, the value of improved short term wind generation forecasts looks to becomes significant once variable renewables generate more than roughly 60% of annual electricity demand. Reductions in average market prices of 5 $/MWh or more then appear plausible as acceptable levels of reliability are recovered. As such, improvements in short-term renewable generation forecasting are likely required in order to justify continued investment in variable renewables as the major means of achieving decarbonisation efficiently and reliably, and these could be achieved by any combination of improved forecasting, dispatch control or on-site firming.

Cost-reliability trade-offs for grid-connected rooftop PV in emerging economies: A case of Indonesia's urban residential households

This study explores the potential of grid-connected rooftop photovoltaic (PV) systems in terms of how they can be better planned and utilised by understanding possible trade-offs between cost and reliability while acknowledging challenges to utility supply security in the context of emerging economies. The study particularly examines the implications of unserved energy targets, PV capacity, and billing deduction factors on grid-connected rooftop PV's trade-offs in terms of total net present cost and unserved energy. This study considers four residential household segments in Indonesia's urban area as a case study, with four cases applied in each segment representing scenarios on PV capacities and billing deduction factors. Using HOMER software, the analyses highlight the role of cost components in trade-offs involving potential PV capacity cases. Systems with maximum PV capacity exhibit cheaper total net present costs than those of half PV capacity within the same unserved energy. While the optimisations pushed PV capacity up to the maximum size across all unserved energies, higher unserved energy resulted in lower grid capacity required to meet demand associated with the system's maximum unserved energy limit. This study provides residential customers and stakeholders with insights to better plan and implement grid-connected rooftop PV systems and policies.

On Optimal Coordinated Dispatch for Heterogeneous Storage Fleets With Partial Availability

This paper addresses the problem of optimal scheduling of an aggregated power profile (during a coordinated discharging or charging operation) by means of a heterogeneous fleet of storage devices subject to availability constraints. Devices have heterogeneous initial levels of energy, power ratings and efficiency; moreover, the fleet operates without cross-charging of the units. An explicit feedback policy is proposed to compute a feasible schedule whenever one exists and scalable design procedures to achieve maximum time to failure or minimal unserved energy in the case of unfeasible aggregated demand profiles. Finally, a time-domain characterization of the set of feasible demand profiles using aggregate constraints is proposed, suitable for optimization problems where the aggregate population behaviour is of interest.

How Sampling and Averaging Historical Solar and Wind Data Can Distort Resource Adequacy

Capacity planning models and resource adequacy assessments have often relied on averaging and sampling techniques that disregard important reasonably expected interactions of weather-based resources. We provide a method to capture the economic value of information and reliability risk from using inadequate sample data to design sustainable systems with high renewable generation. Analysis of long run portfolio cost and sources of uncertainty shows as much as a 16% system cost increase with a 38-fold increase in expected unserved energy when average renewable outputs are modeled rather than a 10 year hourly coincident sample, which illustrates the pitfalls of averaging data and ignoring temporal interdependencies. Investment recommendations can significantly differ depending on which years and how many years are included in the analysis. We show that selecting the wrong year can increase system costs by over 4% with a 7-fold increase in expected unserved energy, failing to meet planned reliability and renewable design targets. It is possible for a single year of coincident load-wind-solar data to reasonably approximate system characteristics; however, the best year changes with renewable penetration.

Optimal battery energy storage system deployment from perspectives of private investors and system operators for enhancing power system reliability

Due to their intermittent nature, high penetration of renewable energy resources (RES) can deteriorate power system reliability. A battery energy storage system (BESS) offers an opportunity to reduce the uncertainty associated with RES and hence improve power system reliability. In the last decade, the cost of BESS has declined so its deployment has become increasingly feasible, but the decision ultimately depends on its Net-Benefit to the investor. Depending on the perspective of the investor, the deployment decision involves discovering the most optimal location, capacity, and technology of BESS. A sensitivity analysis based on the Z-bus matrix is used for selecting the optimal location. In this paper, from the narrow perspective of a private investor, the Net-Benefit function is the energy arbitrage value of BESS minus its overall cost. However, the system operator additionally values BESS because it enables more reliable system operation at reduced operating costs. After examining multiple power system reliability indices, the product of Expected Unserved Energy (EUE) and Value of Lost Load (VoLL) is incorporated into the Net-Benefit function of the system operator. MATPOWER Optimal Scheduling Tool (MOST) is used for testing the proposed Net-Benefit functions on a standard reliability test system (RTS) known as RTS-GMLC. Three diverse case studies are examined in three exploration phases; the first phase helps determine search space whereas the second one narrows it down and the third phase finds the optimal BESS solution. Presented research results demonstrate that, over a period of 15 years, optimal BESS deployment can offer as much as $M164.8 and $M33.6 Net-Benefits to the system operator and private investor respectively.

Coordinated Planning of Electricity and Hydrogen Networks with Hydrogen Supply Chain for Fuel Cell Electric Vehicles

Hydrogen production has become an emerging technology and offered a pathway to sustainable energy utilization in transportation systems. In this paper, we propose a coordinated planning of the electricity network and hydrogen refueling and production station planning strategy for hydrogen fuel cell electric vehicles (FCEVs). In the proposed system structure, a hydrogen supply chain for FCEVs mainly composes hydrogen refueling stations (HRSs), hydrogen production stations (HPSs), hydrogen storage, and hydrogen delivery network. To enhance the system's flexibility, hydrogen delivery combines both gas pipeline delivery and truck logistics delivery. In the mathematical models, the synergistic effect and coupling relationship of the electricity network, gas network, and transportation network are presented. To ensure the efficiency and effectiveness of the hydrogen refueling system, a quality of service (QoS) and reliability assessment method is presented. The proposed methodology is verified in case studies. It is found out that compared with the single delivery methods, the proposed multi-network coordinated planning has the lowest total cost with less unserved energy. Besides, through QoS and reliability assessment, enough hydrogen reserves can be maintained to cope with contingencies.

A tri-level optimal defense method against coordinated cyber-physical attacks considering full substation topology

In recent years, power systems have been facing an increasing risk of malicious attacks. As modern power systems have a greater reliance on communication and automatic control systems, coordinated cyber-physical attacks will cause more serious consequences for the reliable power supply than traditional malicious attacks. To enhance the resilience of power systems against coordinated attacks, a defense method to allocating hardening resources is proposed in this paper considering the full substation topology and the multi-stage response process of power systems. The problem is formulated as a tri-level optimization model involving interactions among the defender, the attacker, and the operator, in which the synergy impact of three attack forms is considered, i.e., physical attacks causing short-circuiting, cyber attacks causing protective relay misoperation, and cyber attacks causing protective relay maloperation. A graph-based analytical model is established to characterize the impact process of such coordinated attacks on power systems. Case studies are conducted on the IEEE RTS 24-bus system. The influence mechanism of coordinated cyber-physical attacks involving protective relays on power systems is systematically revealed. Results show that the proposed method can effectively defend against the above-mentioned coordinated attacks, and enhance the resilience level of power systems. In addition, a sensitivity analysis is implemented on the influence of attack resources and defense resources on the unserved energy at each stage of the system.

Effect of modelling choices in the unit commitment problem

In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the consequences on solution quality. In this study we thoroughly investigated the impacts of simplifications on solution quality and computation time on a benchmark set consisting of almost all the available instances in the literature. We found that omitting the minimum up- and downtime and simplifying the startup cost resulted in a significant quality loss without reducing the computation time. Omitting reserve requirements, ramping limits and transmission limits reduced the computation time, but degraded the solution significantly. However, the linear relaxation resulted in less quality loss with a significant speed-up and resulted in no difference when unserved energy was minimized. Finally, we found that the average and maximum capacity factor difference is large for all model variants.

Analysis of Long-Term Variable Renewable Energy Heavy Capacity Plans Including Electric Vehicle and Hydrogen Scenarios: Methodology and Illustrative Case Study for Turkey

Following COP26, many countries are embarking on decarbonization strategies for the power sector that may include inter alia storage, hydrogen and carbon capture (and storage). There is also significant increase in load that may come in the form of electric vehicles (EV) charging and hydrogen requirement for decarbonization of other sectors. While there is a growing literature around long-term decarbonization strategies, there is still ample room for a practical methodology to rigorously test capacity plans to include a range of options inter alia re-optimization of the mix of renewable technologies, better coordination of the (EV) load from a system perspective, or augmenting the plan with battery energy storage (BESS) and hydrogen. This paper presents our research on EV load and green hydrogen modeling including how they can be integrated into long-term electricity models. We present a methodology that allows planners to undertake a rigorous assessment that can be readily implemented using the World Bank Electricity Planning Model (EPM). The application of the model is illustrated through a case study for Turkey (Türkiye) for 2050. The case study shows how an incumbent policy-driven capacity plan for 2050 that included 33% contribution from variable renewable energy (VRE) may be prone to unserved energy risk during winter months due to seasonal variability of VRE. The analysis goes on to demonstrate how the plan can be reinforced with additional peaking gas turbines, re-optimization of wind and solar, BESS and hydrogen. Coordinated charging of EVs is also shown to bring significant relief to investment requirements.

Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model

Energy communities enable effective coordination among prosumers on pursuing collective targets. This paper focuses on isolated 100% renewable communities, involving individual (controllable appliances and small generators) and collective (wind generators and battery banks) assets. To effectively coordinate the agents involved in these structures, advanced energy management strategies are necessary. This work develops a three-stage day-ahead scheduling strategy for isolated 100% energy communities, involving peer-to-peer transactions among prosumers. The different uncertainties involved are incorporated through a novel stochastic-robust formulation, that results in a computationally tractable optimization framework. To validate the new model, a case study on a six-prosumer benchmark community is analysed. Results reveal the importance of collective assets and peer-to-peer exchanges among prosumers as well as the effectiveness of the developed formulation. The role of batteries is also discussed, helping to reduce the total unserved energy and operating cost by 20% and 19%, respectively, as well as enabling a more efficient use of wind energy. The impact of robustness is also studied, incrementing the expected importable energy by 28% compared to the deterministic case, while the exportable energy from prosumers is notably reduced by 40%. However, uncertainty-aware strategies have a direct impact on operational costs, incrementing the expenditures by 37% when uncertainties are considered.

Protecting Power Transmission Systems against Intelligent Physical Attacks: A Critical Systematic Review

Power systems are exposed to various physical threats due to extreme events, technical failures, human errors, and deliberate damage. Physical threats are among the most destructive factors to endanger the power systems security by intelligently targeting power systems components, such as Transmission Lines (TLs), to damage/destroy the facilities or disrupt the power systems operation. The aim of physical attacks in disrupting power systems can be power systems instability, load interruptions, unserved energy costs, repair/displacement costs, and even cascading failures and blackouts. Due to dispersing in large geographical areas, power transmission systems are more exposed to physical threats. Power systems operators, as the system defenders, protect power systems in different stages of a physical attack by minimizing the impacts of such destructive attacks. In this regard, many studies have been conducted in the literature. In this paper, an overview of the previous research studies related to power systems protection against physical attacks is conducted. This paper also outlines the main characteristics, such as physical attack adverse impacts, defending actions, optimization methods, understudied systems, uncertainty considerations, expansion planning, and cascading failures. Furthermore, this paper gives some key findings and recommendations to identify the research gap in the literature.

Resilience-Oriented Planning of Multi-Carrier Microgrids under Cyber-Attacks

Microgrids are inherently subject to a variety of cyber-physical threats due to potential vulnerabilities in their cyber systems. In this context, this paper introduces a cyber-attack-resilient design of a multi-carrier microgrid to avoid the loss of critical loads. The objective of the proposed model is to minimize the total planning cost of multi-carrier microgrids, which incorporates the investment and replacement costs of distributed energy resources, operation and maintenance costs, peak demand charges, emission costs, unserved energy costs, and potential reinforcement costs to handle cyber-physical attacks. Not only is the proposed multi-carrier microgrid planning approach able to determine the optimal size of multi-carrier microgrids, but it also identifies and reinforces the system to handle cyber-physical attacks by serving critical loads. The proposed multi-carrier microgrid planning model is formulated as a mixed-integer programming problem and solved using the GAMS 24.1 software. To evaluate the effectiveness of the proposed integrated resource planning model, it is applied to a real-world industrial park test-case system. Numerical simulations demonstrate the effectiveness of the resilience-oriented multi-carrier microgrid planning model. Importantly, the simulation results indicate the economic viability of multi-carrier microgrids optimized by the proposed model. Also, the model sensitivity of various decision variables has been analyzed.

Characterisation and mitigation of renewable droughts in the Australian National Electricity Market

In a decarbonising world, the electricity generation mix in Australia’s National Electricity Market (NEM) is likely to be heavily dependent on wind and solar. Designing an electricity system dominated by variable renewable energy generation requires careful examination of periods of low renewable output to ensure storage or other back up generation is sufficient to avoid loss of load. This study uses 15 years of climate and electricity demand data to examine the frequency and nature of the occurrence of low renewable periods. It examines strategies for their mitigation so that unserved energy standards are not breached. We have found that the winter period, May to August, is the time where the NEM is at greatest risk of loss of load. This winter period is when the demand in southern Australian states is higher, solar generation is lower and a series of low wind periods can drain storage. It has been demonstrated that any proposed generation mix reliant on renewable energy generation should be stress tested across a low wind winter, like the complex winter of 2010, not just a single isolated low wind period. Storage was found to be ideal to provide energy for a few hours overnight, but firm dispatchable thermal generation is likely to be a lower cost option than long term storage for extended low wind periods. Diversifying generation with the addition of offshore wind may reduce the need for storage, although the need for floating wind turbines may make this alternative too expensive to add any value in the Australian context.

Private risk and social resilience in liberalized electricity markets

<h2>Summary</h2> Energy-only electricity markets, such as the Electric Reliability Council of Texas (ERCOT), rely on the decentralized investment decisions of market participants to lead to a resource mix providing an efficient level of reliability. During an exceptionally cold winter storm in February 2021, ERCOT experienced shortfalls on an unprecedented scale, with nearly half of the generation fleet experiencing outages at the peak. The depth of the resulting blackouts invites questions regarding the ability of systems relying on decentralized planning to appropriately prepare for and withstand rare events. Based on two mild assumptions, risk aversion among investors and incomplete risk trading, this paper provides an explanation for why decentralized markets are prone to underinvestment in resilience. We describe the nature of the incomplete risk trading that arises in the context of electricity markets and discuss potential remedies, including mandatory contracting obligations for retailers and compensation to end users for unserved energy.

An experimental study of capacity remuneration mechanisms in the electricity industry

After electricity liberalization, the “energy-only market” design lacks effective incentives to invest in new capacity. Across the world, capacity remuneration mechanisms have been taking hold as an alternative to the energy-only market to ensure adequate power generation capacity. The way these designs are characterized reflects the insight from general economic theory on market power and strategic behavior. Indeed, Europe is heading toward a patchwork of different, uncoordinated national mechanisms. In practice, design choices are driven by national policies, needs and constraints. In addition, they are progressively converging toward a market-based mechanism with a forward period and market power mitigation. In this paper, we investigate their efficiency properties in terms of new investments, the reduction in unserved energy frequency and the energy prices of a generic capacity remuneration mechanism that is impervious to the so-called forward capacity market. The results from laboratory experiments confirm that this mechanism gives better incentives to invest in new capacity than the energy-only market alone and provides an empirical understanding of the investment decision process. The forward capacity market contributes to lower energy market prices in peak demand and extra-high peak demand periods and average energy costs if we consider the social cost of unserved energy.

Bi-Level Multi-Objective Planning Model of Solar PV-Battery Storage-Based DERs in Smart Grid Distribution System

This paper presents a novel bi-level multi-objective model for planning solar PV-battery storage (PV-BS) based DERs in Smart grid distribution system (SGDS). The planning of Solar PV and battery storage (BS) in the distribution system presents severe complexities for the system planner due to their inherent attributes. The detailed analysis considering all three planning aspects, namely technical, economics, and environmental, is presented, assisting in the efficient and reliable planning operation. The four test cases are formulated based upon the life cycle cost, unserved energy, penetration level, and social welfare. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level-1</i> proceeds with evaluating the DERs sizing along with penetration level and serves as an input to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level-2</i> for computing the optimal placement for DERs. The proposed model is implemented on the IEEE-33 bus distribution system and solved using Butterfly-PSO (BF-PSO) algorithm. The detailed parametric and sensitivity analysis presented may aid the system planners in terms of planning preview. The simulation results validate the effectiveness of the proposed work by showing significant improvement in bus voltage profiles (up to 4%) and reduction in total power losses (up to 45%). The highest penetration level is observed for Case 2 (70%), which also corresponds to the highest LCC (k <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 147094) and thus results in the lowest emission cost (k <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 4920).