Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

https://www.mdpi.com/1424-8220/20/16/4376/pdf

Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller.

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Optimal Scheduling of Residential Electricity Demand Based on the Power Management of Hybrid Energy Resources

With advancement in technology, there is a huge demand for electrical energy. This paper focuses on consumer's perspective of Demand Response using load shifting technique to minimize the energy consumption during peak hours and to reduce the operating cost of the consumer. This is done by scheduling an hourly energy consumption value for a load, such that the energy consumed during peak hours is low. This optimization problem is solved by using Genetic algorithm. It is considered as a consumer's perspective because the Demand Response process takes place by the consumer's preference to accept or decline the demand signal sent from the utility to control the load. This paper also focuses on automating Demand Response by implementing a remotely controllable system to schedule the load and to control the load on the consumer's side. The scheduled values from Genetic algorithm are stored in ThingSpeak, a cloud platform, so that the controller controlling the load can read the value from ThingSpeak and control the load accordingly. Simulations of Genetic Algorithm were done using MATLAB.

Remotely Controllable Consumer Perspective Demand Response using Genetic Algorithm

The paper focuses on the new opportunities offered by the second-generation smart meters (2G SM) installed in Italy by the Distribution System Operator (DSO) E-Distribuzione, who has started the roll-out of the new metering devices in 2017. Among the innovative features of 2G SM stands out the possibility for the customers to be informed in real time on its consumptions. This feature opens new scenarios with a more active and aware role of the user in the management of energy resources and in the participation in the regulatory initiatives that the DSO can implement. In the paper is presented the regulatory contest and the property of communication between the in-home device (IHD), used to develop demand side management (DSM) algorithms, and the smart meters. With the aim of testing the performances of the Chain-2 and the DSM strategy, a specialized laboratory has been created to simulate three typical Italian apartments. Finally, an example of a strategy for home automation is presented; it can lead to a decrease in costs for the consumption of electricity and it can be considered as a verification for the effectiveness of the new communication between the meter and the customer.

Demand Side Management services for smart buildings with the use of second generation Smart Meter and the Chain-2 of E-Distribuzione

Los grandes usuarios en Argentina tienen una tarifa especifica que establece dos escalones para el precio de la energía, dependiendo si su demanda es superior (o igual) a 300 kW o inferior. En el primer caso, el costo de la energía es mayor al 40 % y es el motivo principal para la aplicación de estrategias de gestión de demanda en los usuarios con una potencia cercana a los 300 kW. Al gestionar el consumo, el beneficio es doble, dado que se reduce significativamente la factura eléctrica al evitar que el usuario exceda los 300 kW y se disminuye el consumo. En este artículo, se propone una gestión óptima de los aires acondicionados de un centro de diagnóstico por imágenes que tiene picos de potencia superiores a 300 kW. La gestión considera a los equipos de aire acondicionado como las únicas cargas controlables y permite minimizar los costos de energía mientras maximiza el confort térmico. La estrategia se basa en un control predictivo de horizontes deslizantes que utiliza un algoritmo genético para resolver el problema de optimización. Además, el control contempla restricciones eléctricas y de temperatura. El desempeño se evalúa contra el control on-off de los aires acondicionados del centro, y los resultados muestran una reducción significativa del costo eléctrico y un mantenimiento del confort debido al recorte de los picos de demanda y a una pequeña disminución de la energía consumida.

Optimizacion del Costo Eléctrico de Grandes Demandas en Argentina por Medio de la Gestión Predictiva del Sistema de Climatizacion

Load control on the consumer side is termed as Demand Response and there is a purpose and urgency to develop new technologies for achieving energy conservation and demand response. There are several DR techniques and algorithms. However, this paper features the study of residential consumer behavior and its response towards Demand Side Management using load-shifting concept. The primary objective of DR is to reduce the overall peak load demand and the operational cost. Genetic Algorithm is involved to the average load profile of the devices used by the residential consumers on the hourly basis. MatLab is used for the simulation purpose and the result achieves substantial savings in cost along with a peak load reduction.

N Pavithra

Papers

Residential demand response using genetic algorithm