Demand Response

In this paper, we develop a framework to optimally manage the time-phased deployment planning of a new technology, namely Advanced Metering Infrastructure, in the Utility industry. Advanced Metering Infrastructure enable two-way communication between residential and commercial customers and the Utility grid, so that energy consumers may respond to price signals during stressful conditions in the grid in a price-sensitive fashion, and reduce the overall stress in the grid. It also enables consumers to shift discretionary energy loads from stressful, peak periods to off-peak periods. We present an end-to-end solution framework for addressing the various analytical challenges that are involved in developing an optimal deployment plan, from a business case development perspective. Our solution framework uses a judicious combination of system dynamics modeling, econometric modeling and mathematical programming based optimization modeling. A system dynamics model is used to estimate the d...

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.

We have studied a low energy building on a campus of the University of California. It has efficient heating, ventilation, and air conditioning (HVAC) systems, consisting of a dual-fan/dual-duct variable air volume (VAV) system. As a major building on the campus, it was included in two demand response (DR) events in the summers of 2008 and 2009. With chilled water supplied by thermal energy storage in the central plant, cooling fans played a critical role during DR events. In this paper, an EnergyPlus model of the building was developed and calibrated. We compared both whole-building and HVAC fan energy consumption with model predictions to understand why demand savings in 2009 were much lower than in 2008. We also used model simulations of the study building to assess pre-cooling, a strategy that has been shown to improve demand saving and thermal comfort in many types of building. This study indicates a properly calibrated EnergyPlus model can reasonably predict demand savings from...

Peak energy demand is made of people’s simultaneous activities around the same period of the day. In the past, utilities made a profit out of peak demand, but the integration of renewable sources of energy and changing patterns in demand mean that peaks are increasingly seen as a problem in terms of cost of energy and carbon emissions. Demand Side Response consists of a set of programmes aimed at shifting peak demand to periods of the day when demand is lower and generation is cheaper.
Whilst in the U.S. Demand Side Response has been used for decades, this is a relatively recent phenomenon in Europe. Following studies in the first decade of the 2000s, which demonstrated future imbalances between demand and supply due to more intermittent renewables and higher peaks from electric vehicles and heat pumps, the regulatory space for Demand Side Response has been expanding. The number of businesses, such as energy aggregators, providing Demand Side Response services has been growing and gaining more and more traction also thanks to national roll-out of smart meters and plans to introduce Capacity Markets in most European countries.
Despite the widespread optimism on Demand Side Response, a comprehensive account of the evidence on the impacts of programmes aimed at shifting peaks of energy demand is missing. This book covers this gap by presenting evidence on a set of Demand Side Response activities, ranging from price-based to incentive-based programmes and policies. It also looks at the actual energy saving impacts of smart meters, collecting evidence from over 30 trials. The book draws on examples of different programmes for both residential and non-residential sectors of electricity demand, including Time of Use tariffs, Critical Peak Pricing Automated Demand Controllers and Ancillary Services. What constitutes peak energy demand? Can price influence the timing of electricity demand? Will automated controllers replace manual forms of Demand Side Response intervention?

Negli ultimi anni, l’integrazione di programmi di Demand Side Management è stata relativamente poco diffusa nei mercati energetici europei. Il Demand Side Management può essere definito come un ampio insieme di azioni che possono venire intraprese dal cliente in risposta a condizioni particolari nel sistema energetico (come ad esempio: periodi di congestione e di picco o prezzi elevati). Questo capitolo si prefigge di esaminare le ragioni che hanno portato ad un basso livello di penetrazione del Demand Side Management in Europa con riflessioni sui fattori che potrebbero influire una maggiore penetrazione in futuro, soprattutto in seguito alll’introduzione di Capacity Mechanisms. A questo scopo, il capitolo si avvale di una revisione della letteratura anche empirica su programmi di Demand Side Management già in essere. I risultati evidenziano come cambi nel sistema normativo, nella regolazione e nella tipologia di investimenti potrebbero modificare il ruolo del Demand Side Management in Europa. Per esempio, l’esperienza britannica indica che un cambiamento nella durata dei contratti di carico potrebbe favorire la partecipazione di una ben più ampia gamma di clienti di quelli  attualmente impegnati nei mercati di Demand Side Management.

Traffic signals are essential traffic management tools in urban conditions. The design of signals has been an evolving science, driven by the need to make best use of available road space, and changing policy objectives, moving away from capacity maximisation or delay minimisation for private vehicles, towards priority operations for public transport, recognising the passenger numbers transported. In addition, many authorities now actively wish to pursue policies aimed at modal shift towards non-motorised and public transport modes, whilst environmental and safety concerns also come into play. Traditional design tools to calculate signal timings in the UK are TRANSYT and LINSIG. These were initially used in two applications for congested corridors in Wolverhampton, aimed at designing demand responsive traffic signals that allow for extra priority for public transport. LINSIG and TRANSYT failed to represent the congested corridor properly. As an alternative design tool a VISSIM micro...

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption. It helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced over all operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management system is with specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. DSM techniques are based on improvement of the load factor and load curve, whereas lowering the expenses.

In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches.

The development smart grids have made the power systems planning and operation more efficient by the application of renewable energy resources, electric vehicles, two-way communication, self-healing, consumer engagement, distribution intelligence, etc. The objective of this paper is to present a detailed comprehensive review of challenges, issues and opportunities for the development of smart grid. Smart grids are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. This paper presents various challenges of smart grid development including interoperability, network communications, demand response, energy storage and distribution grid management. This paper also reviews various issues associated with the development of smart grid. Local, regional, national and global opportunities for the development of smart grid are also reported in this paper. 1. INTRODUCTION Smart grid (SG) technologies are vital to meet world's vast and growing electricity needs. Smart grids (SGs) are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. Micro grids operate at the distribution level, and they are natural innovation zones for the Smart Grid (SG) because they have experimentation scalability and flexibility, and delivers power is a local area. SG contains protection against the cyber attacks, interoperability and designed for pricing in real-time [1]. Super grid is a high voltage DC transmission and capacity to minimize losses and enhance reliability. MGs operates as a standalone or as a grid-connected system. Microgrid (MG) technology is not equipped with automation and communication support. Further work is required to enhance self-healing, reconfigurable, adaptive and predictive capability. MG includes special purpose inverters enabling it to link to the legacy grid and contains special purpose filters build to overcome issues with harmonics, while improving power quality and efficiency [2]. Various characteristics of SG include optimizing the asset utilization and efficient operation is presented in [3]. Increased renewable power penetration, electricity markets participation throughout the world will realize new opportunities for the cost-effective smart grids controls and energy storage at all scales. These changes, coupled with increased consumer awareness and participation will lead to a new paradigm in energy system analysis that must also be accounted for energy security, stability and reliability. The SG will incorporates new networking technology, including controls and sensors, from this the electricity can be monitored in real time and can be made automatic changes which will reduce the energy waste [4].

In this study, a significant literature review on peak load shaving strategies has been presented. The impact of three major strategies for peak load shaving, namely demand side management (DSM), integration of energy storage system (ESS), and integration of electric vehicle (EV) to the grid has been discussed in detail. Discussion on possible challenges and future research directions for each type of the strategy has also been included in this review. For the energy storage system, different technologies used for peak load shaving purpose, which include their methods of operation and control have been elaborated further. Finally, the sizing of the ESS storage system is discussed. For the demand side management system, various management methods and challenges associated with DSM implementation have been thoroughly explained. A detailed discussion on the electric vehicle strategy has also been included in the review, which considers the integration, control and operation techniques for implementing the peak load shaving.

In order to coordinate the scheduling problem between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in multi-stakeholder scenarios, a new bi-level optimal scheduling model is proposed for promoting the participation of BSSs in regulating the IMG economic operation. In this model, the upper-level sub-problem is formulated to minimize the IMG net costs, while the lower-level aims to maximize the profits of the BSS under real-time pricing environments determined by demand responses in the upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA, is put forward by combining a real/integer-coded JAYA algorithm and the branch and bound algorithm (BBA), in which the JAYA and BBA are respectively employed to address the upper- and lower- level sub-problems, and the bi-level model is eventually solved through alternate iterations between the two levels. The simulation results on a microgrid test system verify the effectiveness and superiority of the presented approach.

In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper- and lower- levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.

In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

This article explores the current picture of Slovakia’s and the Czech Republic’s domestic energy market, the national reality concerning decentralization efforts as well as their suitability to achieve it. It assesses the current situation of new technologies, namely smart grids, electric mobility, demand response, and electricity storage technologies. Further, it looks at data protection concerns in the development of new technologies. The article then critically analyses the relevant novel policies in place and projected promising policies for the oncoming years in terms of encouraging regional cooperation, interconnection, consumer empowerment, decentralization, and the deployment of new technologies towards a more secure and “smarter grid”. The article concludes that Slovakia depends largely on its domestic production of nuclear energy and the import of primary energy sources to meet its primary demand. In such a position, the implementation of decentralized electricity generation becomes a priority. In terms of legal environment, Slovakia offers remarkable and suitable conditions to continue developing new technologies in the energy sector. Its regulatory framework presents a valuable basis −although not developed enough− to drive this paradigm shift. As for Czechia’s electricity sector, it is highly competitive, enjoys a relatively low energy dependence (compared to the average of EU countries), and its high level of grid interconnection contributes to energy security. However, in terms of smartening and decentralization, there is still room for policy improvement.

—In this work, we evaluate performance of home energy management system(HEM) by using three meta-heuristic optimization techniques: Harmony search algorithm (HSA), Bacterial foraging optimization (BFO) and Enhanced deferential evolution(EDE). We categorize appliances into three groups on the basis of their energy consumption pattern. Real time pricing (RTP) scheme is used for electricity bill calculation. Our objectives are to minimize electricity cost, energy consumption, reduction in peak to average ratio while maximizing user comfort. However, there exists a trade-off between different objectives. Our simulation results show that there exist a trade-off between user comfort and cost. Results also show that in terms of cost HSA perform better among other techniques.

The changing landscape of the European energy system is fundamentally challenging the role of Distribution System Operators (DSOs). While drastic reforms are not warranted, national and European policy-makers should consider a number of legal and regulatory adjustments. These are at the centre of this new study on the changing role of DSOs.