The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management
The demand for electricity is undergoing considerable spatial and temporal change. With the uptake of efficient technologies and increased electrification, a better understanding of how potential changes in demand patterns can affect network reliability is necessary. We quantify the macro-economic impacts of potential future changes in demand profiles for an electricity network undergoing generation shortages. Applied to Great Britain, potential savings or losses are assessed for changes to peak demands under four different load profiles: (i) current day, (ii) widespread uptake of efficient ap…
Tsinghua University（清华大学）；Institute for Interdisciplinary Information Sciences（交叉信息研究院）
Data analytics and machine learning techniques are being rapidly adopted into the power system, including power system control as well as electricity market design. In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design. More precisely, we follow the idea that consumer's load profile should uniquely determine its electricity rate, which yields a clustering oriented pricing scheme. We first identify the strategic behaviors of malicious users by defining a notion of disguising. Based on this notion, we characterize…
Local electricity markets represent a way of supplementing traditional retailing contracts for end consumers -- among these markets, the renewable energy community has gained momentum over the last few years. This paper proposes a practical and readily to be adopted modelling solution for these communities, one that allows their members to share the economic benefits derived from them. The proposed solution relies on an \emph{ex-post} allocation of the electricity that is generated within energy communities (i.e., local electricity) based on the optimisation of \emph{repartition keys}. Reparti…
In this paper, we consider a future electricity market consisting of aggregated energy prosumers, who are equipped with local wind power plants (WPPs) to support (part of) their energy demands and can also trade energy with day-ahead market (DAM) and energy balancing market (EBM). In addition, an energy aggregator (EA) is established, who can provide the trading gateways between prosumers and the markets. The EA is responsible for making pricing strategies on the prosumers to influence their trading behaviours such that the social benefit of the prosumer community is improved. Specifically, tw…
Community electricity storage systems for multiple applications promise benefits over household electricity storage systems. More economical flexibility options such as demand response and sector coupling might reduce the market size for storage facilities. This paper assesses the economic performance of community electricity storage systems by taking competitive flexibility options into account. For this purpose, an actor-related, scenario-based optimization framework is applied. The results are in line with the literature and show that community storage systems are economically more efficien…
Electricity distribution networks that contain large photovoltaic solar systems can experience power flows between customers. These may create both technical and socio-economic challenges. This paper establishes how these challenges can be addressed through the combined deployment of Community-scale Energy Storage (CES) and local network tariffs. Our study simulates the operation of a CES under a range of local network tariff models, using current Australian electricity prices and current network prices as a reference. We assess the financial outcomes for solar and non-solar owning customers a…
This paper presents an original collaborative framework for power exchanges inside a low voltage community. The community seeks to minimize its total costs by scheduling on a daily basis the resources of its members. In this respect, their flexibility such as excess storage capacity, unused local generation or shiftable load are exploited. Total costs include not only the energy commodity, but also grid fees associated to the community operation, through the integration of power flow constraints. In order to share the community costs in a fair manner, two different cost distributions are propo…
Power systems often rely on natural gas pipeline networks to supply fuel for gas-fired generation. Market inefficiencies and a lack of formal coordination between the wholesale power and gas delivery infrastructures may magnify the broader impact of a cyber-attack on a natural gas pipeline. In this study we present a model that can be used to quantify the impact of cyber-attacks on electricity and gas delivery operations. We model activation of cyber-attack vectors that attempt to gain access to pipeline gas compressor controls using a continuous-time Markov chain over a state space based on t…
Most power systems are increasingly based on distributed energy resources, leading to a strong impact on the electrical grid management. In the case of Portugal, the contribution of renewable energy sources to the electricity generation portfolio is already high and the objective is to achieve 100% by 2050. Most of the new renewable generation capacity will be ensured by distributed photovoltaic generation installed in buildings and therefore the inherent intermittence of photovoltaic output combined with a mismatch with demand profile will challenge the operation and resiliency of the electri…
This paper develops a community resilience optimization method subject to power flow constraints in the Cyber-Physical-Social Systems in Power Engineering, which is solved using a multi-agent-based algorithm. The tool that makes the nexus between electricity generation on the physical side and the consumers and the critical loads on the social side is the power flow algorithm. Specifically, the levels of emotion, empathy, cooperation, and the physical health of the consumers, prosumers are modeled in the proposed community resilience optimization approach while accounting for the electric powe…
To cater the rapidly growing demand for electricity leading to the integration of renewable energy sources in power system. Due to intermittent nature of renewables, it also brings challenges for research community during the planning and operation stage in power system. Therefore it is primary necessity of the community to develop an accurate forecasting technique to solve the intermittency problem. In this report, A forecasting technique is proposed based on ensemble of state of the art forecasting techniques. For performance comparison among the techniques, GEFCom2014 meteorological data ar…
Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a unive…
