(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

With the number of electric vehicles (EVs) on the rise, there is a need for an adequate charging infrastructure to serve these vehicles. The emerging extreme fast-charging (XFC) technology has the potential to provide a refueling experience similar to that of gasoline vehicles. In this article, we review the state-of-the-art EV charging infrastructure and focus on the XFC technology, which will be necessary to support the current and future EV refueling needs. We present the design considerations of the XFC stations and review the typical power electronics converter topologies suitable to deliver XFC. We consider the benefits of using the solid-state transformers (SSTs) in the XFC stations to replace the conventional line-frequency transformers and further provide a comprehensive review of the medium-voltage SST designs for the XFC application.

Extreme Fast Charging of Electric Vehicles: A Technology Overview

One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users' convenience rates, and thermal comfort level.

Optimal Smart Home Energy Management Considering Energy Saving and a Comfortable Lifestyle

Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one.

Home energy management systems: A review of modelling and complexity

In this paper, we consider the operation optimization for a microgrid (MG) aggregator, which can procure energy from various sources including the pool market and local distributed energy resources to serve MG customers. We assume that the MG aggregator sells electricity to customers at a predefined retail rate and it also offers customers various contracts for adjusting their loads. Our design objective is to determine the optimal hourly bids that the MG aggregator submits to the day-ahead market to maximize its profit. To deal with various uncertainties, a risk-constrained scenario-based stochastic programming framework is proposed where the MG aggregator's risk aversion is modeled using conditional value at risk method. The proposed formulation enables customers' demand response (DR) aggregation to be integrated into the operation of the MG aggregator via contractual agreements. This design is not only beneficial for both MG aggregator and customers, but also facilitates the operation of the system operator (SO), since a single entity (i.e., the MG aggregator) is visible to the SO instead of two separate entities (i.e., a MG aggregator and a DR aggregator). Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.

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Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response

Calls to improve customer participation as a key element of smart grids have reinvigorated interest in demand-side features such as distributed generation, on-site storage and demand response. In the context of deregulated market structures, these features can improve flexibility of demand, but at the cost of added uncertainty. Therefore, how to implement these features under deregulated power markets is worth consideration. To address the problems induced by the demand-side participation features together with deregulated electricity markets, this paper presents a new bidding mechanism, which uses Price Elasticity Matrices (PEM) to model the concerned features. Three typical traditional bidding mechanisms are reviewed and compared with the proposed bidding mechanism. This paper also presents an algorithm guaranteeing better convergence to carry out the proposed bidding mechanism. The concept of a stepped supply curve's relative slope is defined in the algorithm. Multiple benefits induced are shown by numerical examples in a day-ahead wholesale electricity pool under real-time pricing.

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A new wholesale bidding mechanism for enhanced demand response in smart grids

Plug-in electric vehicles (PEVs) have become a key factor driving towards smart cities, which allow for higher energy efficiency and lower environmental impact across urban sectors. Industry vision for future PEV includes the ability to recharge a vehicle at a speed comparable to traditional gas refuelling, i.e. <3 min. per vehicle. Such a technology, referred to as ultra-fast charging (UFC), has drawn much interest from research and industry. However, UFC poses unprecedented challenges to existing electricity supply infrastructure due to its large power density, impulsive, and stochastic load characteristics. Planning the locations and electric capacities of UFC stations is critical to preventing detrimental impacts. In particular, efforts must be made of mitigate grid asset depreciation, grid instabilities, and deteriorated power quality. The authors first review planning methods for conventional charging stations. Next, they discuss outlooks for UFC planning solutions by drawing an analogy with renewable energy (RE) source planning. This analogy is based on the similar power density and stochastic characteristics of RE and UFC. While this study mainly focuses on UFC planning from the power grid perspective, other urban aspects, including traffic flow and end-user behaviour, are examined for feasible UFC integration within smart cities.

Integrating ultra-fast charging stations within the power grids of smart cities: a review

This paper presents a new two-step design approach of forward electricity markets, which is highly penetrated with intermittent energy sources, such as wind power, and Demand Response (DR) programs. First, an optimal market timeframe is determined that balances between generation forecast accuracy and customers' responding flexibility, in order to achieve a minimum system cost in forward and reserve energy markets. The timeframe obtained is used as a known parameter in the subsequent design of forward market price. End users' scheduling strategies modeled by price elasticity matrices along with current system conditions are considered in the price calculation process, during which the system cost of the forward energy market is minimized.

Optimization of Forward Electricity Markets Considering Wind Generation and Demand Response

Plug-In Electric Vehicles (PEV) have become a key factor driving towards smart cities, which allow for higher energy efficiency and lower environmental impact across urban sectors. Industry vision for future PEV includes the ability to recharge a vehicle at a speed comparable to traditional gas refueling, i.e., less than $3$ minutes per vehicle. Such a technology, referred to as Ultra-Fast Charging (UFC), has drawn much interest from research and industry. The large power density, impulsive, and stochastic loading characteristics of UFC, however, pose unprecedented challenges to existing electricity supply infrastructure. Planning the locations and electric capacities of these UFC stations is critical to preventing detrimental impacts, including grid asset depreciation, grid instabilities, and deteriorated power quality. In this paper, we first review planning methods for conventional charging stations and then discuss outlooks for UFC planning solutions by drawing an analogy with renewable energy source planning, which presents similar power density and stochastic characteristics as UFC. While this paper mainly focuses on UFC planning from the power grid perspective, other urban aspects, including traffic flow and end-user behavior, are examined for feasible UFC integration within smart cities.

Integrating Ultra-Fast Charging Stations within the Power Grids of Smart Cities: A Review

Emergency reconfiguration can improve distribution systems' reliability by enabling load transfer among substations. Previous studies, although present its operation strategies, seldom explore emergency reconfiguration's effects on distribution system planning. This paper explores how reconfiguration affects planning decisions and what benefits reconfiguration can bring to distribution system planning. For this purpose, two specific planning problems, planning substation capacity and allocating transformers, are studied under the Single-Contingency Policy (SCP). LP models are developed to find the optimal decisions in above problems. The main implications of this paper are: (1) enabling reconfigurable feeders can increase reliability with lower capital investment and operational cost when comparing to reinforcing transformer capacity; (2) the max-load of individual substation depends on its neighboring substations' characteristics; (3) the reliability of a set of well interconnected substations depends on the max-shortage of one of its substations; and (4) allocating transformers according to distribution networks' reconfiguration capability can improve distribution systems' reliability.

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Jiankang Wang

Papers

A new wholesale bidding mechanism for enhanced demand response in smart grids

Integrating ultra-fast charging stations within the power grids of smart cities: a review

Optimization of Forward Electricity Markets Considering Wind Generation and Demand Response

Integrating Ultra-Fast Charging Stations within the Power Grids of Smart Cities: A Review

Emergency reconfiguration and distribution system planning under the Single-Contingency Policy