Real-time Pricing Environment Research Articles

Optimizing Efficiency of Home Energy Management System in Smart Grid using Genetic Algorithm

A next-generation electrical power system known as the "Smart Grid" (SG) uses two-way communication to generate, use, and transport electrical energy. Demand Response (DR) is one of the SG's primary features (DR). Smart meters in DR transmit a pricing signal to the consumer, allowing them to adjust their demand in reaction to the corresponding price signals. Day-Ahead and Time-of-Use are the most widely used load scheduling schemes, however, they deviate from the Real time pricing scheme (RTP). Due to its erratic nature, integrating renewable energy sources like solar and wind is a difficult process in SG. Because of the fluctuations in both energy consumption patterns and power rates, the majority of current methods for managing demand are predicated either on day-ahead or time-of-use pricing rather than real-time pricing. This study describes a load scheduling system that uses a Genetic Algorithm (GA) to classify various users according to their energy use in a real-time pricing environment. Our load scheduling problem is formulated utilizing the knapsack mathematical formulation technique to reduce the electricity expenditure. In order to keep the grid stable and reduce costs, renewable energy is integrated with the grid's energy to lower the Peak-to-Average Ratio (PAR). The efficiency of the suggested algorithm in terms of electricity cost and PAR reduction is supported by simulation results.

Intelligent Multi-Agent Based Multilayered Control System for Opportunistic Load Scheduling in Smart Buildings

Today’s power systems are subject to the high penetration of dynamic load. Volatility and intermittency of the dynamic load demand need to be compensated through optimization and scheduling without compromising user comfort. This paper proposes a multi-agent-based multi-layered hierarchical control system for residential load management under real-time pricing environment. The major objectives are to reduce peak load demand, electricity cost, and user discomfort. In doing so, different types of agents, i.e., price agent $p_{a}$ , sensor agent $s_{a}$ , decision agent $d_{a}$ , load agent $l_{a}$ , and action agent $a_{a}$ , are developed to control residential loads, such as normal load ( nl ) and heavy load ( hl ). To handle price uncertainty, dynamically, optimal stopping rule (OSR) theory has been used. Two variants of OSR are proposed: 1) priority inversion logic-based OSR to subsidize the responsive consumers and 2) maximum energy consumption limit $Q$ -based OSR-Q to maximize the profit of energy retailers. Finally, the proposed mechanism is validated on a set of loads to show the applicability and proficiency under a dynamic environment.

Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming 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.

A fuzzy-based customer response prediction model for a day-ahead dynamic pricing system

Demand side management is an important aspect of the newly established smart grid concept. Consequently, real-time pricing plays a crucial role in controlling the power demand in the grid, as it encourages customers to shift their time-insensitive loads to off-peak periods. However, most solutions do not address the realistic behavior of customers. In this paper, customers’ behavior is modeled based on two main factors; their awareness to the concept of real-time pricing, and their flexibility in shifting their consumption patterns. A Fuzzy logic Customer Response Prediction Model is presented to predict the customers’ demand based on the aforementioned factors and the level of motivation offered by the utility company in terms of a change in price. Unlike most of the research in the field which is concerned with predicting the demand, the core of this model lies in predicting how different customers will behave in a real-time pricing environment based on their levels of flexibility, awareness, and the added motivation, which in turn will shape the overall future demand. Using the presented model, it is expected that customers with higher awareness and flexibility would respond better to dynamic price changes.

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

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.

Collaborative demand response in smart electric grid with virtual system operator

The authors present a novel concept of virtual system operator (VSO) and its mechanism. This mechanism aims to determine load consumption pattern on day-ahead basis which suits all the involved entities individually in real-time pricing environment. The demand profile is scheduled by every consumer by performing demand response (DR) without having to coordinate with other consumers in the system. In the process flow, the VSO which remains in communication with the system operator and the consumer relays information regarding day-ahead electricity tariff to consumers. Based on which the consumers simultaneously perform DR to optimise their electricity bill and forward the schedule to the VSO. This process repeats iteratively until a balanced price profile as well as load schedule is established in the system. Such schedules are capable of reducing dynamics in the electricity market. The proposed concept is demonstrated through three different types of consumers. These consumers realise DR through different objective functions and up to different extents. Ultimately, the load schedules are obtained for all individual users and corresponding price profile which suits every entity of the system.

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Energy Management for Households With Solar Assisted Thermal Load Considering Renewable Energy and Price Uncertainty

In this paper, we investigate the energy scheduling problem for a household equipped with a solar assisted heating, ventilation, and air conditioning, and water heating system in the real-time pricing environment. Our objective is to minimize the electricity cost while maintaining user's thermal comfort requirements. We consider different types of loads with different characteristics, detailed modeling of thermal dynamics, and uncertainty in electricity price and solar energy. The advantage of the proposed design lies in the exploitation of the solar assisted thermal system that can flexibly utilize the energy from the solar source or from the grid during low-price periods to support the home hot water demand, user thermal indoor temperature preference while reducing the electricity cost. We present numerical results to illustrate the effectiveness of our proposed design. Specifically, we show that the proposed design can achieve significant cost saving, allow flexible tradeoff between user comfort tolerance and electricity cost reduction, and efficiently adjust the electricity consumption load profile. We also analyze the influence of solar assisted thermal system factors such as the water tank temperature limit, solar collector size, and weather condition on the achievable cost.

Load Scheduling With Price Uncertainty and Temporally-Coupled Constraints in Smart Grids

Recent years have witnessed the significant growth in electricity consumption. The emerging smart grid aims to address the ever-increasing load through appropriate scheduling, i.e., to shift the energy demand from peak to off-peak periods by pricing tariffs as incentives. Under the real-time pricing environment, due to the uncertainty of future prices, load scheduling is formulated as an optimization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, we propose dual decomposition and stochastic gradient to solve the problem. That is, the primal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, we propose an online approach to further alleviate the impact of price prediction error. Numerical results are provided to validate our theoretical analysis.

전력저장장치를 이용한 태양광주택의 최적부하제어기법

The renewable energy system and the real-time pricing can provide the significant economic advantage for end-user of residential house. However, according to recent studies, high initial cost of renewable energy system such as photovoltaic (PV) system and lack of suitable load control methods adjusting electric power consumption in response to time-varying price are regarded as the major obstruction for introduction of renewable energy system and real-time pricing in residental household. In this paper, we propose automated optimal load control strategy which aim to achieve not only minimizing the electricity cost but also the increase in the utilization rates of PV generation power of residential PV house in real-time pricing environment. Simulation results show that our proposed optimal load control strategy leads to significant reduction in the electricity costs and increase in the utilization rates of power generated by PV system in comparison with the conventional PV house. Therefore, the proposed optimal load control strategy can provide more economic benefit to end-user.