Real-time Electricity Price Research Articles

A heuristically optimized comprehensive charging scheme for large‐scale EV integration

The expectation of large-scale electric vehicles (EVs) charging and discharging load could bring security and reliability challenges to the power system. As a smart load, EV requires an intelligently designed scheduling and pricing algorithm that takes into account the stochastic EVs user behavior, grid charging capacity, battery characteristics, and real-time electricity price variations. In this paper, a multi-objective comprehensive stand-alone solution is proposed considering the time-of-use (TOU) price to intelligently regulate EVs charging/discharging activities. The proposed alternative heuristic charging strategy optimally configures solution indices and provide a tradeoff between considered evaluation parameters, thus mitigating the effect of uncontrolled charging introduced by stochastic EV charge/discharge activities. The objective is to shift the peak hours load to nonpeak hours with reduction in average-to-peak ratio, charging cost minimization and maximize the availability of charging capacity for pledging the traveling plan determined by EV users. Different EVs penetration are tested to validate the performance of the proposed solution under massive EV integration, with a driving pattern obtained from the Beijing National Travel Survey.

A Novel Control Strategy for Improving the Performance of a Nearly Zero Energy Building

This paper deals with one of the most challenging problems in nearly zero energy buildings (nZEBs) of several multifaced and sometimes contradictory objectives that is the energy management. Specifically, aim of the paper is to propose an integrated control strategy (ICS) based on genetic algorithms that can provide an optimal balance between the objectives of energy saving, comfort of the building residents and maximum exploitation of the generated electric energy by the renewable energy sources through the proper utilization of a battery storage system (BSS). The above can be attained by optimizing a comprehensive cost function that considers the most important factors that may affect the performance of an nZEB, i.e., real-time electricity price, generated/consumed electric energy by each device, user preferences, state-of-charge, and energy price of the BSS, weather forecast and the nZEB's construction characteristics. The outcome of the ICS is the proper task scheduling and control of the electric loads as well as the regulation of the BSS operation so as the nZEB's performance is improved. The feasibility and effectiveness of the proposed ICS are verified in a hardware-in-the-loop system and selective experimental results are presented to demonstrate the operational improvements.

Load shifting of a supplier based demand response of multi-class subscribers in smart grid

This paper presents a load shifting mechanism to address the problem of the overloaded electricity consumption requirement of different classes of electricity users, namely, the residential, commercial and industrial users, provided by a supplier in a smart grid. A real-time pricing model based the utility of each user group and the supply cost is given and an alternating direction method of multipliers is used to solve the model for obtaining the optimal real-time electricity price and consumption of each user group. A demand response (DR) algorithm-based different user groups, gives a priority load shifting rule to allocate the surplus optimal or reserved electricity consumption requirements of some user groups to other suppliers to keep the requirements within the capacity of the supplier. Numerical experiment is conducted to analyse the performance of the DR algorithm, which shows that the consumption requirement load shifting mechanism is reasonable and feasible.

Stability of Transactive Energy Market-Based Power Distribution System Under Data Integrity Attack

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

Two-Stage IoT Device Scheduling With Dynamic Programming for Energy Internet Systems

With the rapid evolution of electric systems, there has been a significant demand for energy Internet (EI) systems that allow sustainable and environmentally friendly energy management. Several research efforts regarding EI systems have been aimed at providing reliable, efficient, and cost-effective techniques. In this paper, we propose a novel algorithm and system for real-time electricity pricing and scheduling. Our algorithm consists of a two-stage operation. The first stage performs real-time pricing to determine the maximum electricity consumption while the second stage performs Internet of Things (IoT) device scheduling. In the second stage, the optimization framework for scheduling is modeled as a 0–1 Knapsack problem; therefore, the solutions to the optimization problem are computed using a dynamic programming framework. Through intensive simulations with well-defined parameters, it is verified that the proposed scheme provides several features, especially reductions in electricity bills with the appropriate parameter settings.

Aligning prosumers with the electricity wholesale market – The impact of time-varying price signals and fixed network charges on solar self-consumption

PV-battery systems are currently not operated in an energy system optimal way as their operation heuristic (maximization of self-consumption) is generally unaffected by competitive market signals. To evaluate potential regulatory intervention, we propose a market alignment indicator which measures the relative economic efficiency of a prosumer battery compared to a benchmark system that is completely responsive to wholesale market prices. Investigating the case of PV-battery systems in Germany, we find that scarcity signals transmitted to prosumers improve the market alignment of PV-battery systems while retaining similar levels of self-consumption and autarky rates. Both dynamic prices for generation (time-varying feed-in remuneration) and consumption (real-time electricity prices) can improve welfare, that is lowering consumer expenditures for electricity at the wholesale market. The effectiveness of the respective instrument mix depends on the relative levels of the feed-in tariff, the grid consumption to be saved and the solar generation costs. Accordingly, increasing fixed network charges can have a significant positive impact on the market alignment of prosumer batteries if combined with dynamic prices, as they change the relative composition of retail prices.

Optimal Consumer Efforts and Operational Costs Based Analysis for a Smart Grid

This paper deals with the comparison approach of operational cost and consumer efforts for the smart grid system. There are two objectives considered for the smart power system i.e. the minimization of the operational cost and consumer efforts of demand responses. Normally, consumers do not want to make much effort for reducing or increasing their loads that is why, this research is done for reducing consumer effort, beside reducing the fuel cost of thermal generations. Consumer efforts have been considered through the real-time pricing of electricity. Mainly a multi-objective algorithm such as Non-Dominated Sort Genetic Algorithm-II (NSGA-II) has been utilized to determine the optimum Pareto solution. Also, optimal values of NSGA-II has been compared with Epsilon Multi Objective Genetic Algorithm (ε-MOGA). There are three different scenarios reflected in this paper such as case 1, case 2, and case 3 for understanding the importance of consumer efforts. The case 1 considers the minimum operational cost and maximum customer efforts. The case 2 considers the moderate operational cost and moderate customer efforts and the case 3 considers the maximum operational cost and minimum customer efforts. The computer simulation has been performed by the MATLAB® software.

Smart energy coordination of autonomous residential home

The smart grid technology permits the revolution of the electrical system from a conventional power grid to an intelligent power network which has led the improvements in electrical system in terms of energy efficiency and sustainable energy integration. This study presents the energy management/coordination scheme for domestic demand using the key strategy of smart grid energy efficiency modelling. The structure consists of combining renewable energy resources, photovoltaic (PV) and wind power generation connected to the utility grid with energy storage system (ESS) in an optimal control manner to coordinate the power flow of a residential home. Based on the demand response schemes in the framework of real-time electricity pricing, this work designs a closed-loop optimal control strategy that is created by the dynamic model of the ESS to compute the system performance index, which is formulated by the cost of the energy flows. A dynamic distributed energy storage strategy (DDESS) is implemented to optimally coordinate the energy system, which reduces the total energy consumption from the main grid of more than 100% of the load demand. The designed model introduces a payback scheme while robustly optimising the energy flows and minimising the utility grid's energy consumption cost.

Joint Energy-QoE Efficient Content Delivery Networks Using Real-Time Energy Management

In this paper, a joint optimization method has been proposed for energy cost and the user's perceived quality-of-experience (QoE) within a content delivery network (CDN). The proposed algorithm has been developed with an objective to minimize the total operational cost using real-time electricity pricing as well as the integration of green energy resources from the smart grid. To solve the joint optimization problem, a linear programming and a differential evolution algorithm have been used to provide a tradeoff between operational cost saving and computational complexity. We also formulate the joint problem as a noncooperative game in which the CDN providers act as players. A group utility function has been defined for the players and it is shown that a Nash equilibrium exists for the joint quality and energy cost saving when each CDN provider has single cluster or multiple clusters. Numerical results are presented to evaluate and validate the aforementioned solutions. These results illustrate significant operational/energy cost reductions while optimizing user's perceived QoE for a CDN or multiple CDN providers with respect to conventional methods.

A Lyapunov optimization approach to energy-efficient content delivery networks

A content delivery network (CDN) supports fast growing services (such as streaming media) for end users and uses request a routing algorithm to enhance the users’ perceived quality of experience (QoE). This working process can increase the power consumption and eventually the operational cost of the CDN system substantially. In addition, a smart grid facilitates real-time electricity pricing for the consumers as well as the integration of green energy resources into the electrical grid for improving the system efficiency. In this paper, we first take into account these key functionalities of a smart grid to propose a real-time energy management algorithm for reducing the operational cost within a CDN cluster. We then define the proposed stochastic optimization algorithm for jointly optimizing the energy cost and perceived quality within a CDN system based on the popular Lyapunov-drift methodology. Numerical results are presented to evaluate and validate the aforementioned solution. The obtained results indicate large operational and energy cost savings for a single CDN or more than one CDN providers over a conventional method that only optimizes the users’ perceived QoE.

Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

Hedging Strategies for Heat and Electricity Consumers in the Presence of Real-Time Demand Response Programs

Real-time pricing (RTP) is a component of some demand response programs (DRPs) in electricity markets. This paper proposes a real-time price-based demand response management model for heat and power consumers. In the proposed DRP, the responsive electrical load can vary in different time intervals. Real-time price risk can be shared among market participants sensibly by implementing several RTP hedge contracts. In this context, supply sources include energy from the real-time market, self-production facilities, as well as RTP hedge contract instruments. The model is introduced for electricity consumers equipped with combined heat and power systems, power-only unit, boiler unit, and heat buffer tank. Optimal hedging strategies, including hedge contract choices and hedged load percentages, can be acquired by solving the model. The proposed model makes it possible to achieve minimum energy cost while considering uncertainties in real-time electricity prices. The price uncertainty is addressed through robust optimization for minimizing the worst case electricity and heat demand procurement cost while flexibly adjusting the solution robustness. Numerical simulations are provided, which confirm the applicability and effectiveness of the proposed model.

Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning

This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.

Application of Economical Building Management System for Singapore Commercial Building

Smart buildings form an integral part of the smart grid that lowers electricity cost. This objective can be achieved by using suitable enhanced algorithms and techniques. The motivation of this paper is the request for an economical decision-making system from a commercial company in Singapore. This paper presents an economical building management system (EBMS) concept inspired by the building management system and machine learning. EBMS improves the decision-making process through rule-based algorithm within the system. Machine learning algorithm is included as part of EBMS to enhance the computational calculation for the electricity distribution. It includes the function of self-making decision for purchasing electricity based on forecasting system for electricity price or real-time electricity price data when facility managers are not present. EBMS uses multiagent system to communicate, interact, and negotiate with multiple agents for energy supply and demand in the building. Simulation studies have shown the potential of EBMS concept to provide a cost reduction solution up to 61.42% savings for smart buildings. Better electricity purchasing contract option can be decided based on the forecasting system for building power consumption.

Using the analog ensemble method as a proxy measurement for wind power predictability

Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S.

Prospect Theory-Based Active Consumer Behavior Under Variable Electricity Pricing

Active consumers who engage in energy consumption, production and provide ancillary services in a dynamic and interactive manner will be an integral part of the future grid. This paper models and analyzes interaction between active consumers and aggregators with a specific focus on consumer actions in response to real-time electricity pricing and their impacts on grid voltage. First, this paper introduces a unique prospect theory-based consumer behavior model that captures wide range of consumers each with their individual preferences. Second, the interaction between consumers and aggregators is modeled as a Stackelberg game. However, unlike existing game theoretic efforts that assume rational behavior of consumers, the prospect theory-based models systematically incorporate realistic consumer behavior including irrationality. Another unique aspect of the formulation is the inclusion of the physical grid constraints (in terms of voltage violations) within the analysis. In contrast to prior approaches that limit themselves to economic aspects, the proposed techno-economic perspective provides an understanding of the impact of large scale penetration of active consumers on the physical grid. The proposed model is analyzed using simulations on the IEEE 69 bus test system and the impact of irrationality on both consumer/aggregator payoffs and voltage violations is quantified.

Empowering end-use consumers of electricity to aggregate for demand-side participation

End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology.

A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network

We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.

Optimal Energy Scheduling Method under Load Shaping Demand Response Program in a Home Energy Management System

Abstract With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm.

Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids

In the context of smart grids, residential air conditioners, as the major contributors to home electricity bills and loads on electrical grids, need to be not only energy-efficient, but also grid-responsive to relieve power supply-demand imbalance. Existing demand response control strategies for residential air conditioners focus on single-speed type and mainly adopt temperature set point reset to respond to hourly day-ahead electricity prices. This study represents the first attempt to directly control the operating frequency of inverter air conditioners in response to high-granularity electricity price signals, i.e., 5-minute real-time electricity prices, using model predictive control method. A simplified room thermal model in the stochastic state-space representation and performance maps of an inverter air conditioner are developed and integrated for predicting the coupled thermal response of an air-conditioned room. A demand response-enabled model predictive controller is designed based on the coupled model to optimally control the operating frequency of the inverter air conditioner while taking account of all the influential variables including weather conditions, occupancy, and 5-min real-time electricity prices. A Kalman filter is adopted to estimate the unmeasurable variables and remove the noises in measurements. A TRNSYS-MATLAB co-simulation testbed is developed to test the thermal and energy performances of the model predictive controller. Results show that compared to the PID controller, the demand response-enabled model predictive controller can implement automatic and optimal precooling to improve occupant’s thermal comfort at the beginning of occupancy. Moreover, it can also reduce average power consumption during peak demand periods by 17.31% to 38.86% compared with the PID controller and reduce all-day electricity costs by 0.42% to 22.16%. The frequency-based model predictive control method enables residential inverter air conditioners to be more grid-friendly and cost-efficient.