Smart Grid, Smart City, Customer Research Report

Prepared by the UTS: Institute for Sustainable Futures as part of the AEFI consortium for Ausgrid and EnergyAustrali

Low Cost and Reliable Energy Management in Smart Residential Homes Using the GA Based Constrained Optimization

Recently smart grids have given chance to residential customers to schedule operation times of smart home appliances to reduce electricity bills and the peak-to-average ratio through the demand side management. This is apparently a multi-objective combinatorial optimization problem including the constraints and consumer preferences that can be solved for optimized operation times under reasonable conditions. Although there are a limited number of techniques used to achieve this goal, it seems that the binary-coded genetic algorithm (BCGA) is the most suitable approach to do so due to on/off controls of smart home appliances. This paper proposes a BCGA method to solve the above-mentioned problem by developing a new crossover algorithm and the simulation results show that daily energy cost and peak to average ratio can be managed to reduce to acceptable levels by contributing significantly to residential customers and utility companies

Optimal behavior of responsive residential demand considering hybrid phase change materials

Due to communication and technology developments, residential consumers are enabled to participate in Demand Response Programs (DRPs), control their consumption and decrease their cost by using Household Energy Management (HEM) systems. On the other hand, capability of energy storage systems to improve the energy efficiency causes that employing Phase Change Materials (PCM) as thermal storage systems to be widely addressed in the building applications. In this paper, an operational model of HEM system considering the incorporation of more than one type of PCM in plastering mortars (hybrid PCM) is proposed not only to minimize the customerâ s cost in different DRPs but also to guaranty the habitantsâ  satisfaction. Moreover, the proposed model ensures the technical and economic limits of batteries and electrical appliances. Different case studies indicate that implementation of hybrid PCM in the buildings can meaningfully affect the operational pattern of HEM systems in different DRPs. The results reveal that the customerâ s electricity cost can be reduced up to 48% by utilizing the proposed model.The work of M. Shafie-khah and J.P.S. Catalão was supported by FEDER funds through COMPETE and by Portuguese funds through FCT, under FCOMP-01-0124-FEDER-020282 (Ref. PTDC/EEA-EEL/118519/2010) and UID/CEC/50021/2013, and also by the EU 7th Framework Programme FP7/2007-2013 under Grant agreement No. 309048 (project SiNGULAR)

Simulation modeling for energy consumption of residential consumers in response to demand side management.

Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load to a cheaper off-peak period. Consequently, when designing incentives most DR literature assumes consumers always minimize total electricity cost when facing energy consumption decisions. However, in practice, it has been shown that customers often override financial incentives if they feel strongly about the inconvenience of load-shifting arrangements. In this dissertation, an energy consumption model based on consumers‟ response to both cost and convenience/comfort is proposed in studying the effects of differential pricing mechanisms. We use multi-attribute utility functions and a model predictive control mechanism to simulate consumer behavior of using non-thermostatic loads vi (prototypical home appliances) and thermostatically controlled load (HVAC). The distributed behavior patterns caused by risk nature, thermal preferences, household size, etc. are all incorporated using an object-oriented simulation model to represent a typical residential population. The simulation based optimization platform thus developed is used to study various types of pricing mechanisms including static and dynamic variable pricing. There are many electric utilities that have applied differential pricing structures to influence consumer behavior. However, majority of current DR practices include static variable pricings, since consumer response to dynamic prices is very difficult to predict. We also study a novel pricing method using demand charge on coincident load. Such a pricing model is based on consumers‟ individual contribution to the monthly system peak, which is highly stochastic. We propose to use the conditional Markov chain to calculate the probability that the system will reach a peak, and subsequently simulate consumers‟ behavior in response to that peak. Sensitivity analysis and comparisons of various rate structures are done using simulation. Overall, this dissertation provides a simulation model to study electricity consumers‟ response to DR programs and various rate structures, and thus can be used to guide the design of optimal pricing mechanism in demand side management

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Optimal Distribution Reconfiguration and Demand Management within Practical Operational Constraints

This dissertation focuses on specific aspects of the technical design and operation of a `smart\u27 distribution system incorporating new technology in the design process. The main purpose of this dissertation is to propose new algorithms in order to achieve a more reliable and economic distribution system. First, a general approach based on Mixed Integer Programming (MIP) is proposed to formulate the reconfiguration problem for a radial/weakly meshed distribution network or restoration following a fault. Two objectives considered in this study are to minimize the active power loss, and to minimize the number of switching operations with respect to operational constraints, such as power balance, line ow limits, voltage limit, and radiality of the network. The latter is the most challenging issue in solving the problem by MIP. A novel approach based on Depth-First Search (DFS) algorithm is implemented to avoid cycles and loops in the system. Due to insufficient measurements and high penetration of controllable loads and renewable resources, reconfiguration with deterministic optimization may not lead to an optimal/feasible result. Therefore, two different methods are proposed to solve the reconfiguration problem in presence of load uncertainty. Second, a new pricing algorithm for residential load participation in demand response program is proposed. The objective is to reduce the cost to the utility company while mitigating the impact on customer satisfaction. This is an iterative approach in which residents and energy supplier exchange information on consumption and price. The prices as well as appliance schedule for the residential customers will be achieved at the point of convergence. As an important contribution of this work, distribution network constraints such as voltage limits, equipment capacity limits, and phase balance constraints are considered in the pricing algorithm. Similar to the locational marginal price (LMP) at the transmission level, different prices for distribution nodes will be obtained. Primary consideration in the proposed approach, and frequently ignored in the literature, is to avoid overly sophisticated decision-making at the customer level. Most customers will have limited capacity or need for elaborate scheduling where actual energy cost savings will be modest

Modelling dynamic demand response for plug-in hybrid electric vehicles based on real-time charging pricing

© The Institution of Engineering and Technology. Based on the benefits of real-time pricing both to individual users and the society as a whole, this study introduces a real-time charging price (RTCP) mechanism supported by an intelligent charging management module into plug-in hybrid electric vehicles (PHEVs) charging environment. The optimal RTCP is executed by a distributed algorithm using a utility model to maximise the whole charging system welfare. The willingness-to-charge parameter is derived to reflect the charging preferences of PHEV users and their different responses to the RTCP. Several scenarios are established to discuss the effect of both the RTCP and willingness-to-charge on charging load. The simulation results show that reasonable charging will be realised based on the optimal RTCP mechanism

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints