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

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy

Recent techniques used in home energy management systems: a review

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.info:eu-repo/semantics/publishedVersio

An efficient water flow control approach for water heaters in direct load control

Tank water heaters (WHs) are present in a prevailing number of European households. Serving as energy buffers WHs have come under the spotlight of various direct load control (DLC) programs over the last few decades. Although DLC has proven to be an efficient measure towards daily peak demand shaving, the payback effect might lead to a new peak in the grid. This payback phenomenon takes place every time a group of WHs under DLC is permitted to catch up. If not handled properly. This paper presents a novel real-time water flow control approach for domestic water heating systems aiming at decreasing the payback effect of DLC actions. We identify possible control strategies based on an analysis of the water system's thermal dynamics. We formulate the problem of optimal water flow control in terms of minimum WH payback demand and maximum user comfort satisfaction. User comfort is formalized by an integral energy characteristic. Simulations show that water flow control can significantly mitigate the DLC payback effect by reaching the fair compromise between energy savings and discomfort of an end-user

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Performance of Smart Homes for participating in Electricity Markets

Devido ao crescente consumo de energia proveniente de residências, o comportamento dos consumidores de Smart Homes vem sendo estudado nos últimos anos, com o objetivo de otimizar a eficiência energética e o consumo de energia. Além disso, é necessário otimizar o consumo de energia da casa para minimizar custos e reduzir as emissões de gases. Atualmente, o Mercado de Energia Elétrica tem se mostrado muito mais competitivo devido ao surgimento de fontes renováveis ​​de energia e à participação ativa do consumidor no mercado, utilizando programas de demand response. O objetivo deste projeto é desenvolver e melhorar um código-fonte para permitir a gestão da demanda de uma casa inteligente, incluindo geração de energia renovável, veículo elétrico e outros aparelhos inteligentes e dispositivos/electrodomésticos elétricos. Além disso, a redução do custo esperado do consumo de energia e o aumento do conforto do consumidor são considerados como metas do projeto.Due to the rising energy consumption of residential consumers, smart home consumers' behaviour is being studied in the last years to achieve optimal energy efficiency and power consumption. Also, there is a need to optimize house energy consumption to minimize costs and reduce gas emissions. Nowadays, Electricity Market has been much more competitive due to the rising of renewable energy sources and consumer's active participation in the market, using demand response programs. This project aims to develop and improve a source code to allow demand management of a Smart Home, including renewable energy generation, electric vehicle and other smart appliances and electrical devices. Furthermore, the reduction of the expected cost of energy consumption and the rise of consumer's comfort are considered as goals for the project

Demand-Side Flexibility in Power Systems:A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

In recent years, environmental concerns about climate change and global warming have encouraged countries to increase investment in renewable energies. As the penetration of renewable power goes up, the intermittency of the power system increases. To counterbalance the power fluctuations, demand-side flexibility is a workable solution. This paper reviews the flexibility potentials of demand sectors, including residential, industrial, commercial, and agricultural, to facilitate the integration of renewables into power systems. In the residential sector, home energy management systems and heat pumps exhibit great flexibility potential. The former can unlock the flexibility of household devices, e.g., wet appliances and lighting systems. The latter integrates the joint heat–power flexibility of heating systems into power grids. In the industrial sector, heavy industries, e.g., cement manufacturing plants, metal smelting, and oil refinery plants, are surveyed. It is discussed how energy-intensive plants can provide flexibility for energy systems. In the commercial sector, supermarket refrigerators, hotels/restaurants, and commercial parking lots of electric vehicles are pointed out. Large-scale parking lots of electric vehicles can be considered as great electrical storage not only to provide flexibility for the upstream network but also to supply the local commercial sector, e.g., shopping stores. In the agricultural sector, irrigation pumps, on-farm solar sites, and variable-frequency-drive water pumps are shown as flexible demands. The flexibility potentials of livestock farms are also surveyed

Model Predictive Control for Demand Response of Thermostatically Controlled Loads

Charakteristickým rysem moderní energetiky je narůstající podíl výroby elektřiny z obnovitelných zdrojů. To přináší řadu výhod z pohledu kvality životního prostředí. Výroba elektřiny z obnovitelných zdrojů má však výrazně stochastický charakter a integrace většího množství takto vyrobené elektřiny do elektrizační sítě není možná, pokud nebudou vytvořeny nové metody řízení spotřeby elektřiny, nové technologie pro skladování elektrické energie a vyspělá řídicí a komunikační infrastruktura. Na straně spotřeby elektrické energie připadá významný podíl termostaticky řízeným spotřebičům. Ty jsou navíc obvykle těsně propojeny s velkými tepelně akumulačními kapacitami. Jsou proto zvláště vhodné pro řízení spotřeby elektřiny a nákladově efektivní akumulaci energie. Z této motivace vychází zaměření této disertační práce na pokročilé algoritmy pro řízení termostatických spotřebičů.Jakékoliv řízení nutně předpokládá, že existuje vhodný řídicí signál, kterým můžeme chování řízené soustavy ovlivňovat. V této práci pracujeme s nepřímým řídicím signálem: cenou elektřiny proměnnou v reálném čase. Tento koncept je používán v řadě pilotních projektů v USA i v EU. Z řady hledisek je tento koncept výhodný: zákazníci si mohou sami rozhodnout, jak na proměnnou cenu budou reagovat bez toho, že by jejich komfort byl ohrožen. Rovněž tak není nutné instalovat složitá rozhraní pro přímé ovládání spotřebičů a monitorování jejich stavu. Návrh vhodných algoritmů pro to, jak reagovat na proměnné ceny však zůstává stále do značné míry otevřeným problémem. Tato práce je zaměřena na dva aspekty tohoto problému.První část práce se zabývá problematikou řízení jednotlivých velkých termostatických spotřebičů, které reaguje na proměnnou cenu elektřiny. Tyto spotřebiče jsou zde popsány obecně jako lineární časově proměnné systémy a jejich řízení je navrženo jako lokální ekonomické prediktivní řízení. Tento ekonomický prediktivní regulátor musí vzít v úvahu časově proměnný charakter řízené soustavy. Tím, že provádí lokální ekonomickou optimalizaci, napomáhá tento regulátor udržet rovnováhu výroby a spotřeby v elektrizační soustavě. Tato část práce vznikla v rámci projektu H2020 SmartNet a jako případovou studii používá jedno z pilotních experimentálních zařízení tohoto projektu: vyhřívaný plavecký bazén. Časová proměnnost matematického modelu tohoto bazénu pramení ze změn součinitele přestupu tepla mezi vodou a vzduchem v závislosti na rychlosti větru.Druhá část práce je zaměřena na menší termostatické spotřebiče, které sice mají jednotlivě zanedbatelný příkon, mohou však hrát významnou roli, pokud je jejich větší počet sdružen dohromady. Struktura navrhovaného řídicího systému je hierarchická. Ekonomický prediktivní regulátor na vyšší rovině řízení reaguje na proměnnou cenu elektřiny a mění žádané hodnoty termostatů na nižší rovině. Cíl řízení je stejný jako v první části práce: cena provozu celé skupiny spotřebičů je minimalizována a to napomáhá udržení rovnováhy v síti. Vzhledem k velkému počtu spotřebičů však není možné, aby prediktivní regulátor pracoval s modely všech jednotlivých spotřebičů, ale bylo nutné vyvinout a ověřit sdružený model dynamiky celé skupiny. Tento model je nelineární a ekonomický prediktivní regulátor musí řešit úlohu nelineárního smíšeného celočíselného programování. Efektivita navržené strategie řízení byla prokázána pomocí simulačních experimentů.Increasing the share of renewable electricity generation is a characteristic feature of modern energy systems. Renewable electricity generation has important environmental benefits, however, it is also marked by significant stochasticity and its large scale integration into power grid is not possible without new methods for control of electricity consumption, new energy storage technologies and communication infrastructure. Thermostatically controlled loads represent a significant share of total electricity consumption and they are often tightly connected with large thermal storage capacities. For these reasons they can be used for controlling electricity consumption and cost effective energy storage. This motivates the focus of this thesis on advanced control algorithms for thermostatically controlled loads.Any control requires a suitable control signal. In this thesis, an indirect control signal is used - the role of the control signal is played by variable electricity price. This concept is considered in many pilot projects both in the USA and in the EU. It has certain advantages: the customers can choose the preferred strategy for responding to the needs of the grid, so their comfort is not compromised; also there is no need to install significantly more complex interfaces for direct control of the loads and monitoring of their states. However, the design of suitable control algorithms for responding to variable prices is still a largely open problem. The thesis focuses on two aspects of this problem.The first part of the thesis considers the control of a single large thermostatically controlled load that responds to the price signal. This load is described by a linear time varying system and a local economic model predictive controller is designed for it. This controller must account for the time varying dynamics of the controlled load. By performing local economic optimization this controller helps to balance supply and demand in the electricity grid. This part of the thesis was created within the framework of H2020 SmartNet project and it considers one of the project pilot demonstrations: heated swimming pool. The time varying character of the model of this pool is due to the changes of the heat transfer coefficient between water and air depending on the wind speed.The second part of the thesis focuses on smaller thermostatically controlled loads. They are negligible individually, but they can play an important role if a larger population is aggregated. The structure of the proposed control system is hierarchical. Economic model predictive controller in the upper level responds to varying electricity price and changes the temperature setpoints of the thermostats in the lower level. The objective of the control system is the same as in the first part of the thesis: the cost of the operation of this population is minimized and this helps to keep the balance in the grid. However, the high number of the loads does not allow individual modelling of each load in the model predictive controller and an aggregate model had to be developed and tested. This model is non-linear and economic model predictive controller has to solve mixed integer non-linear optimization problem. The effectiveness of the proposed control strategy was demonstrated by simulation