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Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI) due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. These findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.

Due to changes in electric distribution grid operation, new operation regimes have been recommended. Distribution grid optimal power flow (DOPF) has received tremendous attention in the research community, yet it has not been fully adopted across the utility industry. Our paper recognizes this problem and suggests a development and integration procedure for DOPF. We propose development of DOPF as a three step procedure of 1) processing the grid, 2) obtaining a tractable solution, and 3) implementing multiple solution algorithms and benchmarking them to improve application reliability. For the integration of DOPF, we demonstrate how a DOPF federate may be developed that can be integrated in a co-simulation environment to mimic the real-world conditions and hence improve its practicality to be deployed in the field. To demonstrate the efficacy of the proposed methods, tests on IEEE 123-bus system are performed where the usage of tractable formulation in DOPF algorithm development and its comparison to the benchmark solution are demonstrated.

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit1. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner's willingness to trade-off between savings and amenity in form of a slider, where the EV owner's amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer's transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed.

Despite extensive research in the past five years and several successfully completed and on-going pilot projects, regulators are still reluctant to implement peer-to-peer trading at a large-scale in today's electricity market. The reason could partly be attributed to the perceived disadvantage of current market participants like retailers due to their exclusion from market participation - a fundamental property of decentralised peer-to-peer trading. As a consequence, recently, there has been growing pressure from energy service providers in favour of retailers' participation in peer-to-peer trading. However, the role of retailers in the peer-to-peer market is yet to be established as no existing study has challenged this fundamental circumspection of decentralized trading. In this context, this perspective takes the first step to discuss the feasibility of retailers' involvement in the peer-to-peer market. In doing so, we identify key characteristics of retail-based and peer-to-peer electricity markets and discuss our viewpoint on how to incorporate a single retailer in a peer-to-peer market without compromising the fundamental decision-making characteristics of both markets. Finally, we give an example of a hypothetical business model to demonstrate how a retailer can be a part of a peer-to-peer market with a promise of collective benefits for the participants., Comment: 4 figures, 2 tables, accepted for publication in iScience (Cell Press)

After a century of relative stability of the electricity industry, extensive deployment of distributed energy resources and recent advances in computation and communication technologies have changed the nature of how we consume, trade, and apply energy. The power system is facing a transition from its traditional hierarchical structure to a more deregulated model by introducing new energy distribution models such as peer-to-peer sharing for connected communities. The proven effectiveness of P2P sharing in benefiting both prosumers and the grid has been demonstrated in many studies and pilot projects. However, there is still no extensive implementation of such sharing models in today's electricity markets. This paper aims to shed some light on this gap through a comprehensive overview of recent advances in the P2P energy system and an insightful discussion of the challenges that need to be addressed in order to establish P2P sharing as a viable energy management option in today's electricity market. To this end, in this article, we provide some background on different aspects of P2P sharing. Then, we discuss advances in P2P sharing through a systematic domain-based classification. We also review different pilot projects on P2P sharing across the globe. Finally, we identify and discuss a number of challenges that need to be addressed for scaling up P2P sharing in the electricity market followed by concluding remarks at the end of the paper., Comment: 33 pages, 6 figures, 4 tables

This paper analyzes the pessimistic effect on the inherent load shifting potential (LSP) of buildings due to the participation in the reserve market. A generic model-based optimization approach is deployed, which uses a validated dynamic model along with its experienced external and internal disturbances, to quantify the LSP in the presence of various price signals. The theoretical maximum LSP is obtained using a base energy price without the provision of ancillary services (AS). The deviation from the base case LSP is observed after including the time varying energy and the reserve price from the spot market. Factors affecting the LSP are found to be as: (1) physics of the model, (2) the nature of price signal and (3) the competency of the reserve price with respect to the energy price. Due to its simple formulation, low computation requirements, and modular nature, the method proposed in this paper can easily be deployed by retailers and system operators for the assessment of monetary incentives as well as qualified load type for various demand response (DR) services in liberalized markets.

This paper proposes a robust demand-side control algorithm in a smart grid environment for heating, ventilation and air conditioning (HVAC) systems. A robust model predictive control (RMPC) scheme in a receding horizon fashion is deployed, which optimizes electricity cost and capacity market participation of the HVAC system, while satisfying comfort and operational constraints of the building and utility, respectively. Thermal load uncertainties experienced by the HVAC system are included to perform a realistic assessment of the developed controller. The National Electricity Market of Singapore (NEMS) is used as a case study and the developed RMPC scheme is tested for various price signals and scenarios. Numerical simulation results show the effectiveness of the developed framework to be readily adopted by utilities -- interested in realizing a grid-friendly and economicaly eficient demand response (DR) strategy.