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

Residential Demand Response using Electricity Smart Meter Data

The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited. The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem". This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC

Analysis of Power Quality Constrained Consumer-Friendly Demand Response in Low Voltage Distributions Network

Load management using demand response (DR) in a low voltage distribution network (LVDN) offers an economically profitable business platform with peak load management. However, the inconvenience caused to the consumer in depriving their devices and the low levels of associated incentive have contributed to lower consumer acceptance for DR programs in the community. However, with the increasing number of controllable consumer loads, a residential-level DR program is highly plausible in the short to medium term. Further, additional DR capabilities (including ancillary services) are likely to improve the remuneration potential for participants in DR. Considering the perspective of a distribution network operator (DNO), any service useful for maintaining the stable and secure operation of an LVDN will always be appreciated. Thus, in addition to DR\u27s peak load management potential, any further contribution in maintaining power quality (PQ) in the network considered as an ancillary service to DNO will create a profitable business opportunity. Firstly, primary PQ management tasks in an LVDN are maintaining voltage profile and reducing harmonics. With the advancement in the consumer electronics market, increased penetration of nonlinear low carbon technologies (LCTs) based loads at the consumer-side, will increases the harmonic content in the LVDN. While consumer devices may have non-threatening levels of harmonic components, they can still cause issues by accumulating at the main feeder when the additive nature of harmonics are considered. Further, and in respect to harmonics, total harmonic distortion (THD), as a universal indicator, may not be a deterministic measure of the impact of harmonics due to THD’s dependency on the magnitude of fundamental current. Moving to the voltage issue, in an electrical network, it is required to maintain the voltage level of all nodes in the network between regulated tolerance levels. However, during peak load hours, the voltage at the end of a radial feeder may drop below the tolerance level. The corollary is also an issue. A light loading scenario on the same feeder with a higher penetration of solar photovoltaic distributed generators (SPVDG) injecting active power can create a voltage rise scenario. While consumer loads/loading are responsible for these PQ issues in the network, there is no direct obligation on residential level consumers to manage them as long as they are individually operating within the regulation limits. However, a DR option can utilize PQ’s dependency on loads to provide additional service to DNO to mitigate any PQ violations. The DR program\u27s success is critically dependent on consumer participation. It also becomes essential to operate the program with a minimum level of consumer inconvenience. Therefore, a proposal for micromanaging consumer load on an LVDN while considering consumer inconvenience and attaining PQ objectives is thus the theme of this thesis. This research proposes a PQ constrained consumer-friendly DR (PQ-C-DR) program that can provide additional ancillary PQ management services along with conventional DR capabilities. Due consideration is given to minimize consumer inconvenience while operating DR to ensure social acceptability and equity. Harmonic levels in the network are essentially integrated as harmonic heating constraints to maintain stable levels of harmonics in LVDN. A DR in conjunction with a co-ordinated incremental and ‘fair’ curtailment algorithm is introduced to manage the voltage levels in the radial LVDN. A sensitivity study of the proposed algorithm is performed on an urban distribution network model under different operating scenarios. This thesis introduces a new algorithmic dimension in applications for load management to ancillary services (PQ management) using DR. The PQ-C-DR will favour consumer comfort while profiting all stakeholders involved, which essentially creates a win-win scenario for all network participants – essential in DNO/consumer negotiations to achieve wider DR engagement. Improving the profitability of DR by providing additional service(s) is beneficial to both customers and retailers. Furthermore, the DNO benefits from delaying additional peak and PQ management related investments, which could essentially improve the utilization factor of the network

An Overview of Demand Response : From its Origins to the Smart Energy Community

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Prosumer communities and relationships in smart grids: A literature review, evolution and future directions

Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a "prosumer." Prosumers' participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions

InovGrid - benefits and new business models under a smart grid context

CEMSOver the next decades, Portugal will face the large-scale introduction of smart-grid technology. With new “smart” requirements, EDP Distribuição will eventually re-define its busines model. Consumption and energy efficiency data was analyzed, resulting in findings of cost and energy savings. To ensure the capture of those benefits and a smoothening of smart grid development, the new role of EDPD as an active distribution management system is presented. Last but not least, the consumer will be enlightened in his role and the reaction towards a dynamic pricing structure which will culminate in a new paradigm of an active Demand Response environment

Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor (DFF) and Agility Factor (AF), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience