Time-varying Electricity Prices Research Articles

Dynamic energy scheduling for end-users with storage devices in smart grid

In this paper, we endeavor to address the problem of dynamic energy scheduling scheme for end-users with storage devices in smart grid. An end-user with an energy storage device is developed, which draws energy from multiple energy sources: local energy suppliers and external power grid. Our goal is to minimize the end-user’s total costs (including energy usage cost and storage cost) over long-term time under uncertainty (time-varying electricity price, uncertain energy output and demand). Concerning the energy storage cost, the resulting problem is modeled as a stochastic optimization case. Following this trend, based on the improved Lyapunov optimization, a dynamic energy scheduling algorithm with lower complexity is developed. It is theoretically proved that the proposed algorithm can make the optimization target asymptotically close to optimum. Simulation results are given to demonstrate that proposed algorithm can significantly save total costs than existing schemes where energy scheduling without the energy storage cost constraint.

Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.

Optimization of Power Dispatch With Load Scheduling for Domestic Fuel Cell-Based Combined Heat and Power System

This study proposes an optimization scheme of thermal power and electric power dispatch integrated with load scheduling for the domestic fuel cell-based combined heat and power (DFCCHP) system. The scheme is implemented in home energy management systems installed in smart homes. To provide accurate energy cost evaluation for the optimization scheme, the nonlinear electric efficiency characteristics of the fuel cell are approximated with a polynomial expression. Along with the inclusion of thermal power dispatch in the scheme, the nonlinear relationship between the thermal power and the electric power of the fuel cell is incorporated to design temperature constraints in the DFCCHP system. On top of the nonlinearity and complexity of the fuel cell, residential electric load scheduling and other energy sources including the power grid, PV panels, and battery energy storage are considered to ensure that the scheme takes a comprehensive energy management approach. Because of the nonlinearity that exists in the modeling of the DFCCHP system, a mixed-integer nonlinear programming formulation is utilized to solve the optimization problem. The scheme is tested in a day-ahead environment with time-varying electricity prices and natural gas prices. The optimization aims to minimize the electricity cost and natural gas cost. It is shown in the simulation that optimization scheme dispatches the electric power and thermal power in an optimal way so that the energy cost due to time-varying electricity prices and natural gas prices is minimized. The electricity cost optimization puts both power purchase and power selling into consideration. It is also shown in the simulation that the household loads are scheduled in an optimal way to the time slots with lower electricity prices in accordance with the optimal thermal and electric power dispatch.

Optimization of electrical energy waste in house using smart appliances management System-A case study

A substantial quantity of energy is consumed by residential buildings. The appliance runs on a set schedule in most of the houses. As a result, the appliance remains switched ON whether user need it or not. This results in significant energy waste and high electricity bill. To address this problem a smart energy management system (SEMS) has been proposed that generates switch ON/OFF control actions for appliances depending on physical characteristics. The SEMS optimize the energy waste and energy cost by reducing the operative time of the appliances without affecting the user comfort. The SEMS further reduces the electricity bill, if public utility offer the time varying electricity price for the residential consumers. A residential building microgrid system with smart appliances, rooftop mounted photovoltaic system, energy storage (electric car, and battery) linked to the public utility is regarded to assess the performance of the SEMS. The performance of the SEMS is tested for both flat and real time electricity price system. The potentiality and functioning of SEMS are verified by measuring energy and environmental performance, as well as user comfort and acceptance of the control system, throughout the course of a normal day. The findings show a potential for energy savings of 31% and a electricity energy cost reduction by 35%. The appliance smart scheduling and management of appliances according to the renewable power generation or the grid electricity cost are reflecting the novality and originality of the proposed SEMS system.

A Control Strategy Based on Deep Reinforcement Learning Under the Combined Wind-Solar Storage System

In a deregulated environment, the renewable energy producers will face the challenge of how to increase their revenues under uncertainties of power generation and time-varying electricity price. In traditional power network scheduling, prediction and optimization are two independent processes, which easily leads to information loss and modeling error. To deal with the uncertainty and realize an end-to-end controller, this article proposes an energy storage system control model (ESSCM) in the scene of the combined wind-solar storage system. The proposed ESSCM using deep reinforcement learning (DRL) algorithm is trained by interacting with the massive environment of a power grid without requiring the assumption on the uncertainties. It learns from scratch to realize the coordination operation of with wind power and photovoltaic power in a combined system, further maximize the benefits of the combined system in the electricity market. One state-of-the-art DRL algorithm, namely double deep Q-network, is used to formulate the proposed ESSCM. Numerical results illustrate that the proposed approach can effectively accommodate the uncertainty and bring high revenues to the combined system.

When Internet of Things Meets e-Health: An Indoor Temperature Monitoring and Control Approach

Ambient temperature is closely related to human health. Maintaining a comfortable and stable temperature is crucial for the treatment, rehabilitation and daily e-health care of patients, where the Internet of Things has been widely applied for this purpose. However, the existing research always separates the network design from temperature monitoring and control. In order to provide better e-health services, we design a solution to minimize the cost of energy consumption and delay. This work discusses a cyber-physical design for an indoor temperature monitoring system using a wireless sensor network. All sensors dynamically adjust the sleep/wake duty cycles and select the optimal anycast routing according to the sensed temperature. In addition, IoT-enabled heating, ventilation, and air conditioner (HVAC) systems for indoor temperature control have attracted unprecedented attention. HVAC can be connected to the Internet for weather and time-varying electricity price data. This work discusses the problem of minimizing the total energy cost of the HVAC system while keeping the indoor temperature within a prefixed range. The key idea is to leverage the time-varying electricity prices and do precooling/preheating using HVAC. The simulation results show that our temperature monitoring and control algorithm outperforms other heuristic schemes.

Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems

Recently, home energy management systems (HEMS) are gaining more popularity enabling customers to minimize their electricity bill under time-varying electricity prices. Although they offer a promising solution for better energy management in smart grids, the uncoordinated and autonomous operation of HEMS may lead to some operational problems at the grid level. This paper aims to develop a coordinated framework for the operation of multiple HEMS in a residential neighborhood based on the optimal and secure operation of the grid. In the proposed framework customers cooperate to optimize energy consumption at the neighborhood level and prevent any grid operational constraints violation. A new price-based global and individualized incentives are proposed for customers to respond and adjust loads. The individual customers are rewarded for their cooperation and the network operator benefits by eliminating rebounding network peaks. The alternating direction method of multipliers (ADMM) technique is used to implement coordinated load scheduling in a distributed manner reducing the computational burden and ensure customer privacy. Simulation results demonstrate the efficacy of the proposed method in maintaining nominal network conditions while ensuring benefits for individual customers as well as grid operators.

Design of structured control policy for shared energy storage in residential community: A stochastic optimization approach

For energy storage shared by multiple residential consumers who are using electricity based on time-varying price and equipped with solar photovoltaic panels, this study is motivated to design an efficient control policy that allows individual consumers to determine operational decisions to realize economic and feasible energy sharing. Although shared energy storage has been considered a promising and practical solution for sharing energy, a proper control policy is required for realizing the expected benefits and advantages of energy sharing via shared energy storage because of the stochastic nature of fluctuated electricity demand load, intermittent solar power generation, and time-varying electricity price in addition to the complex dynamics existing in consumers’ behavior. This study intends to design a structured control policy that is uniquely designed to allow consumers to share energy with energy cost-saving and less solar power spillage. We develop a mathematical optimization model that can be formulated to efficiently find the designed control policy. Numerical experiments are conducted by simulating the derived control policy using real historical data comprising 14 residential houses in New York and 11 residential houses in Texas. Through the numerical experiments, we validate the feasibility and evaluate the performance of the proposed control policy to demonstrate the practicality. The results of the numerical experiments show that the proposed control policy enables consumers to cost-efficiently share energy while efficiently utilizing solar power generation to meet their electricity demand load without the excessive use of electricity from the grid to save energy costs.

Incentivized self-rebalancing fleet in electric vehicle sharing

With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid.

Building HVAC control with reinforcement learning for reduction of energy cost and demand charge

Energy efficiency remains a significant topic in the control of building heating, ventilation, and air-conditioning (HVAC) systems, and diverse set of control strategies have been developed to optimize performance, including recently emerging techniques of deep reinforcement learning (DRL). While most existing works have focused on minimizing energy consumption, the generalization to energy cost minimization under time-varying electricity price profiles and demand charges has rarely been studied. Under these utility structures, significant cost savings can be achieved by pre-cooling buildings in the early morning when electricity is cheaper, thereby reducing expensive afternoon consumption and lowering peak demand. However, correctly identifying these savings requires planning horizons of one day or more. To tackle this problem, we develop Deep Q-Network (DQN) with an action processor, defining the environment as a Partially Observable Markov Decision Process (POMDP) with a reward function consisting of energy cost (time-of-use and peak demand charges) and a discomfort penalty, which is an extension of most reward functions used in existing DRL works in this area. Moreover, we develop a reward shaping technique to overcome the issue of reward sparsity caused by the demand charge. Through a single-zone building simulation platform, we demonstrate that the customized DQN outperforms the baseline rule-based policy, saving close to 6% of total cost with demand charges, while close to 8% without demand charges.

Online Unsupervised Occupancy Anticipation System Applied to Residential Heat Load Management

Human preferences and lifestyles significantly impact buildings' energy consumption. Consequently, a better understanding of occupants' behavior is crucial to decrease energy consumption and maintain occupants' comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants' thermal comfort while decreasing the heating energy consumption.

Predictive control co-design for enhancing flexibility in residential housing with battery degradation

Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational constraints and pushing the system to its efficiency and performance limits in an integrated fashion. The optimality criterion minimises system cost considering time-varying electricity prices and battery degradation. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results from a low-fidelity model show substantial carbon emission reduction and bill savings compared to an a-priori sizing approach.

Coordinated Multi-Time Scale Optimal Control of Building Energy Systems for Demand Response Considering Forecast Uncertainties

As an economical way to address the great challenge from increasing renewable energy generation of intermittent nature, demand response is attracting increasing attention recently. Predictive scheduling is widely used to optimize energy dispatch of building energy systems for demand response, since it can minimize cost considering time-varying electricity price. However, it probably cannot satisfy energy demands and achieve optimized demand response in actual operation due to forecast uncertainties. In this study, a coordinated multi-time scale optimal control strategy is therefore proposed to optimize energy dispatch between building energy systems for demand response considering forecast uncertainties. The control strategy coordinates a stochastic scheduling scheme and a real-time optimal control scheme using coordinating control variables. The selection of coordinating control variables is comprehensively analyzed. The optimal start time of scheduling optimization horizon is identified. Forecast uncertainties are quantified based on real meteorological data. The proposed control strategy was tested and evaluated on the energy system of the Zero Carbon Building in Hong Kong. Results show that it is essential and beneficial to consider forecast uncertainties and coordinate stochastic scheduling with real-time optimal control to ensure satisfaction of energy demands and minimization of energy cost in actual operation when providing demand response.

Quality-Aware Video Streaming for Green Cellular Networks With Hybrid Energy Sources

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage

Considering a scenario where residential consumers are equipped with solar photovoltaic (PV) panels integrated with energy storage while shifting the portion of their electricity demand load in response to time-varying electricity price, i.e., demand response, this study is motivated to analyze the practical benefits of using shared energy storage in residential communities. The main objective of this study is to compare individual and shared energy storage operations economically (in terms of electricity cost) and operationally (in terms of energy storage use) with various parameter settings. We propose mathematical optimization models formulated to find optimal energy operations of individual and shared energy storage so that the best practices can be compared. Through the analysis of the optimal shared energy storage operations resulting from the mathematical optimization model, we intend to discover underlying patterns that can be used for developing efficient control policies tailored to shared energy storage use. We conduct numerical experiments using real historical data, and the results show that shared energy storage results in electricity cost saving with higher utilization compared to individual energy storage. Cost savings and energy storage utilization improvements up to 13.82% and 38.98%, respectively, exist when using shared energy storage instead of individual energy storage. We find that the maximum charging/discharging rate parameters have the most significant effect on individual and shared energy storage settings. We provide useful insights about how to assign residential consumers to shared energy storage and how to control changing and discharging shared energy storage by multiple consumers.

Optimal HVAC Control for Demand Response via Chance-Constrained Two-Stage Stochastic Program

This study is interested in the application of demand response (DR) to building energy management via heating, ventilation and air conditioning (HVAC) controls. The main objective of this study is to develop an optimization model to appropriately control HVAC operations to minimize cost in response to time-varying electricity prices and intermittent solar power generation. The main idea is to adjust electricity consumption associated with HVAC operations while ensuring the thermal comfort of occupants. In particular, we propose a two-stage stochastic program formulated to find an optimal HVAC operations schedule that comprises proactive and reactive controls to minimize energy cost against the stochastic nature in electricity prices, solar power generation, and weather conditions. Specifically, we introduce a chance constraint that can be appropriately integrated with the proposed optimization model to ensure thermal comfort at the desired level. To validate and evaluate the proposed approach, numerical experiments are designed to compare the proposed approach with the benchmark control policies designed to simulate the practice of the general purpose thermostat and the algorithm proposed by relevant study with various parameter settings. The numerical experiments show that the proposed approach results in significant cost savings while capturing optimal HVAC operations.

An efficient decomposition approach for the low-energy scheduling of a straight multiproduct pipeline

This paper proposes a hybrid-time mixed integer linear programming model for low-energy scheduling of single-source multiproduct pipelines. The model divides the time horizon into slots by event points of batches reaching stations and fixed points of electricity price changing. Through this division, some practical factors are easily modelled as linear items, including time-varying electricity price and flowrate limits related to interfaces. The key technology for this model is the initialization of all time points’ sequence, so a decomposition approach is developed to efficiently seek satisfactory sequence of all time points. Its superiority is validated by two real-world cases.

Electrolyzer modeling and real-time control for optimized production of hydrogen gas

We present a method that operates an electrolyzer to meet the demand of a hydrogen refueling station in a cost-effective manner by solving a model-based optimal control problem. To formulate the underlying problem, we first conduct an experimental characterization of a Siemens SILYZER 100 polymer electrolyte membrane electrolyzer with 100 kW of rated power. We run experiments to determine the electrolyzer’s conversion efficiency and thermal dynamics as well as the overload-limiting algorithm used in the electrolyzer. The resulting detailed nonlinear models are used to design a real-time optimal controller, which is then implemented on the actual system. Each minute, the controller solves a deterministic, receding-horizon problem which seeks to minimize the cost of satisfying a given hydrogen demand, while using a storage tank to take advantage of time-varying electricity prices and photovoltaic inflow. We illustrate in simulation the significant cost reduction achieved by our method compared to others in the literature, and then validate our method by demonstrating it in real-time operation on the actual system.

A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes

The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.

Stochastic optimization for flow-shop scheduling with on-site renewable energy generation using a case in the United States

On-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be well investigated. In this paper, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setup. The first stage provides optimal schedules to minimize the total completion time. The second stage determines the energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. The power demand of the production is met by on-site renewable generation, supply from the main grid, and energy storage system. An epsilon-constraint algorithm integrated with L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers. Our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy saves energy costs significantly. While, utilizing solar energy can reduce more.