This paper presents a novel energy-management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. We assume that the microgrid takes part in a pool market and responds actively to the electricity price to maximize its profit by scheduling its controllable resources. To address various uncertainties, a risk-constrained scenario-based stochastic programming framework is proposed using the conditional value at risk method. The designed model is solved by two levels of stochastic optimization methods. One level of optimization is to submit optimal hourly bids to the day-ahead market under the forecast data. The other level of optimization is to determine the optimal scheduling using the scenario-based stochastic data of the uncertain resources. The proposed energy management system is not only beneficial for the microgrid and customers but also applies the microgrid aggregator and virtual power plant (VPP). The results are shown to prove the validity of the proposed framework.

The coordination of wind power and hydro power is an effective way for generating companies to enhance the ability of wind power dispatch and to avoid imbalance charges. However, specifying the probability distributions of random variables can be challenging for decision makers (DMs). This paper proposes a bidding strategy for wind farms and hydro stations in a generating company using interval optimization. Pessimistic preference ordering and DMs' degrees of pessimism are also adopted in the optimization to compare interval numbers. Variations in wind power, the day-ahead energy price, and the intrahour energy price are then considered and represented by interval numbers instead of probability distributions. Compared with stochastic optimization, interval optimization does not need an exact probability distribution for the random variables. Moreover, it can reduce computational complexity as well as determine and optimize profit intervals, as illustrated in the numerical example presented herein.

Abstract:With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users' controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The "broad controllable loads" management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.

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Summary This paper presents a novel multi‐objective energy management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. The fluctuation of the grid tie‐line power flow is regarded as one of the optimal objectives in order to decrease the power exchange between the microgrid and the main grid caused by intermittent and variable renewable resources. The uncertainty of renewable energy and loads is also considered in this paper. Stochastic optimization method is implemented for the output of renewable resources and controllable loads. Using the ε‐constrained and weighted sum method, the multi‐objective operation optimization problem, which includes the minimum microgrid operation costs, fluctuations of the grid tie‐line power, and the risk management, is converted to a single‐objective mixed‐integer linear optimization problem. The scheduling is achieved for controllable units such as diesel generators, batteries, controllable loads, and grid tie‐line power under various weights. The main contribution of the study is to achieve the energy management for a microgrid with regulation of the grid tie‐line power fluctuations and peak shaving. Copyright © 2016 John Wiley & Sons, Ltd.

A novel videophone system for the elderly-care application is proposed. Based on the detailed analysis of the elderly’s physical and psychological characteristics, a TV-based caring videophone system for the elderly is developed: an embedded multimedia device is designed to implement the interactive video and audio processing and IP-based communication, in which TV is adopted as the display terminal to achieve a low-cost but high-quality service. Considering the user’s convenience, many personalized designs, such as photo-based address book, photo-click-dialing, and touch pad based remote controller, are developed to make the proposed videophone system more intuitive and easy to use for the elderly. Based on Support Vector Machine (SVM) algorithms, an evaluation model is also developed with the data collected from the embedded multimedia device. It is useful to evaluate the physical and psychological health of the elderly.

Current cost allocation methods require generating companies (GENCOs) to afford spinning reserve (SR) costs according to their energy production rather than the impact on grid stabilization. The differences in generator reliability and forecast accuracy of renewables cause difficulty in quantifying the contribution of individual factors on the SR requirements (SRRs). First, this paper employs a reliability-constrained unit commitment (RCUC) model to determine the SRR and SR costs according to the grid reliability. Then, a cost allocation method is proposed to allocate these SR costs based on risk contribution theories. The risk contribution theories, marginal contribution, and stand-alone contribution are employed to measure the effect of individual risk factors on grid safety. The cost allocation method is demonstrated and discussed in the IEEE-RTS. The proposed risk contribution method can quantify the impacts of risk factors on grid safety and allocate SR costs into them according to their contributions. Additionally, this risk-based cost allocation method can encourage GENCOs to enhance the reliability level of generators and continuously improve the forecast accuracy of renewables to lower SR costs.