Real-time Electricity Price Research Articles

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling.

Real-Time Differential Pricing Scheme for Active Consumers with Electric Vehicles

Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs. However, the anticipated increase in EV usage presents opportunities as well as challenges for utilities. This paper models EV charging behavior of active consumers who strategize their EV charging schedule to minimize the charging cost. The electric utility employs a temporal and spatially varying real-time pricing scheme that minimizes peak-to-average load ratio by influencing charging behavior of multiple active consumers. These active consumers decide their optimal charging strategy based on real-time electricity prices in order to minimize their EV charging cost. A model predictive control (MPC)-based optimal pricing scheme for electric utility and optimal charging strategy for active consumers is proposed. Effects of the proposed approach on load profile is verified using simulations.

Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems With Solar Renewable Energy

In this paper, a novel optimal energy storage control scheme is investigated in smart grid environments with solar renewable energy. Based on the idea of adaptive dynamic programming (ADP), a self-learning algorithm is constructed to obtain the iterative control law sequence of the battery. Based on the data of the real-time electricity price (electricity rate in brief), the load demand (load in brief), and the solar renewable energy (solar energy in brief), the optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established. A new analysis method of the iterative ADP algorithm is developed to guarantee the convergence of the iterative value function to the optimum under iterative control law sequence for any time index in a period. Numerical results and comparisons are presented to illustrate the effectiveness of the developed algorithm.

Demand response under real‐time pricing for domestic households with renewable DGs and storage

With the increasing penetration of small-scale distributed generators (DGs) and storage units into the domestic areas and the emergence of real-time pricing (RTP) of electricity, residents have more opportunities to obtain a cost-effective energy management through the participation of demand side management. Unlike existing online solutions, this paper presents an alternative one-day-ahead energy dispatch solution to achieve the economic benefits of meeting the demand with minimised electricity purchase cost by optimising the DG and storage utilisation efficiency in the presence of RTP. The performance of the proposed solution is assessed through a comparative study by carrying out simulation experiments for a set of operational scenarios. The numerical result demonstrates that the proposed energy dispatch solution can well meet the demand requirement with significantly reduced electricity purchase cost. With the recognition that the prediction of RTP and DG output can often be inaccurate, the robustness of the solution is further verified under prediction errors.

Coordinated Optimal Operation Method of the Regional Energy Internet

The development of the energy internet has become one of the key ways to solve the energy crisis. This paper studies the system architecture, energy flow characteristics and coordinated optimization method of the regional energy internet. Considering the heat-to-electric ratio of a combined cooling, heating and power unit, energy storage life and real-time electricity price, a double-layer optimal scheduling model is proposed, which includes economic and environmental benefit in the upper layer and energy efficiency in the lower layer. A particle swarm optimizer–individual variation ant colony optimization algorithm is used to solve the computational efficiency and accuracy. Through the calculation and simulation of the simulated system, the energy savings, level of environmental protection and economic optimal dispatching scheme are realized.

Automated demand response system in lighting for optimization of electricity cost

Daylighting is an important factor in improving visual comfort and energy efficiency. Lighting control using daylighting can reduce energy consumption in buildings. This thesis proposes an automatic demand response system for lighting based on wireless sensor networks (WSN) in order to reduce the peak electricity demand according to the stage of electricity rate with real-time pricing (RTP), time of use pricing (TOUP), and critical peak pricing (CPP). The proposed system automatically controls the slat angle of the venetian blind with a cut-off angle according to the altitude of the sun, automatically executing light dimming according to measured current luminance to remove an unpleasant glare caused by daylighting. The target illuminance of area at this time is set at a minimum illuminance required for the work execution in the office during the time zone where the electricity load is high to save the lighting energy cost, while a maximum illuminance is set during the time zone where the electricity load is low according to the real-time electricity pricing stages obtained through the advanced metering infrastructure (AMI) in order to improve the work efficiency of the occupants. In this study, two testbeds having the same environments, as well as a control system targeting a fixed illuminance per price system, were established. The illuminance energy consumption and cost were then measured and the effect of the proposed illuminance system was evaluated.

Real-time Electricity Pricing: TOU-MPC Based Energy Management for Commercial Buildings

An adaptive real-time electricity pricing and optimal control system based on energy management of buildings is presented. This is made in the framework of a smart-grid according to energy demand and time-of-use (TOU) electricity tariff in conjunction with a model predictive control (MPC). The developed model is considered as a multiple single input single output MPC system that acts at a different rate of TOU- electricity tariffs (off-peak, standard and peak). A smart selective timer switching system links one to the other. The performance of the model design is expressed through the simulation results of the adaptive TOU-MPC for commercial buildings.

Data Perturbation-Based Sensitivity Analysis of Real-Time Look-Ahead Economic Dispatch

In this paper, the sensitivity of look-ahead economic dispatch in real-time power markets with respect to data perturbation is studied. In the look-ahead dispatch optimization problem, a small change in the data that are used for setting the interspatial and temporal equality/inequality constraints and the objective function may negatively affect normal operations, such as the calculation of real-time wholesale electricity prices and operating costs. This could lead to more distorted prices and larger operating costs with the look-ahead dispatch than with a static dispatch that use data for a single future time. We perturb Karush–Kuhn–Tucker conditions of the look-ahead dispatch optimization formulation and then, using them, derive a linear sensitivity matrix that assesses the impact of data corruption on look-ahead dispatch. This matrix illustrates the changing optimal solution of look-ahead dispatch subject to potential corruption in various types of spatial and temporal data—generator's bidding cost coefficients, capacity limits for generators and transmission lines, ramp rates and the estimate of initial generation output with ramp constraints, and multiple-time series of the forecast load for the look-ahead horizon. The results of the simulation are illustrated with numerical examples in the IEEE 14-bus system.

Market efficiency assessment under dual pricing rule for the Turkish wholesale electricity market

This study analyses the price dynamics of day-ahead and real-time electricity prices following the implementation of dual-pricing legislation in Turkey, to understand the legislation's impact on arbitrage opportunities and market efficiency. The convergence of prices between the day-ahead forward and the real-time markets analysed in order to determine whether persistent price differences between the two markets exist. Arbitrage opportunities exist if there is a persistent difference between prices in the day-ahead forward and real-time market. Markets are considered to be efficient if it is not possible for market participants to earn an excess profit through exploitation of price differences. Furthermore, we examined how the ex-post risk premium changes over time using rolling estimations and find that after the implementation of dual pricing, the risk premium increased significantly for day and peak hours where the demand is relatively higher compared to night hours. As market participants have more experience regarding the dynamics of the market, the difference between real-time and day-ahead forward prices converges to zero since the dual-pricing regime enforce market participants to forecast accurately by punishing the forecast error.

Performance and feasibility analysis of electricity price based control models for thermal storages in households

Electricity price based control models for thermal storages to balance fluctuations of price have become increasingly important. A number of previous studies in the field of demand side management deal with price based load control to balance the power grid. However, inadequate attention has been paid to comfort and profitability issues of end users. Therefore, insufficient solutions towards profitability and comfort issues may be a serious barrier to demand response. The aim of our paper is to analyse the performance and feasibility of electricity price based control models for thermal storages in households taking into account aspects of comfort. We simulated and compared existing control models and our models. The influence of different models and volatility of the real-time electricity price on the energy cost and electricity consumption of studied loads (i.e. water heater, freezer) have been estimated. While the cost and electricity reduction calculations do not take into account comfort issues, a performance calculation methodology has been developed. The performance is ensured when by minimized temperature change, as compared to maximum comfort settings, the cost reduction/electricity saving is maximized. The control models showing the best performance (incl. electricity or cost savings) under different comfort situations are described.

Real-time electricity pricing for industrial customers: Survey and case studies in the United States

Electricity prices change substantially over time in the wholesale markets. The fluctuations are mainly caused by power grid states, fuel price fluctuations, and market conditions. Real-time pricing (RTP) is an effective way to reduce risks utility companies face from volatile electricity prices. RTP allows participating customers to reduce electric bills by changing use patterns. This paper analyzes the pricing components and characteristics of representative RTP programs for industrial customers based on publicly available information. Our main goal is to study the reasons behind their success and failure. Knowing the details about RTP programs increases customers' awareness of their advantages and assists them in deciding whether they should enroll. It also helps utility companies develop new programs or improve existing ones. Case studies in the manufacturing sector are presented and the savings of RTP are compared with another popular dynamic pricing program, time-of-use (TOU) pricing under different scenarios. The results show that the savings by switching from flat rates to TOU and RTP are highly program dependent. Eighteen out of the 35 base-case scenarios resulted in positive savings by switching to TOU, and 29 out of 35 base-case scenarios resulted in positive savings by switching to RTP.

Cost-Benefit Comparison of a Time-of-Use Tariff and Real-Time Pricing of Electricity Associated with Automated HVAC Load Management Strategies in Bank Across Mainland Portugal

Cost-Benefit Comparison of a Time-of-Use Tariff and Real-Time Pricing of Electricity Associated with Automated HVAC Load Management Strategies in Bank Across Mainland Portugal

Coordination of energy storage systems and DR resources for optimal scheduling of microgrids under uncertainties

Wind energy integration into microgrids, has introduced new challenges to energy management systems because of its intermittent behaviour. Owing to continuous changes in generation and consumption, the energy price is volatile. This study proposes a new two-stage stochastic framework for day-ahead scheduling of microgrids. Uncertainties associated to wind power generation, real-time electricity price and load demand are considered. Different scenarios are generated using autoregressive moving-average method and then are reduced using fast-forward technique. On the first stage, the microgrid master controller determines the procured energy from the day-ahead market and commitment states of distributed energy resources (DERs). In the second stage, the purchased energy from the real-time market and schedules of committed DERs are obtained. The problem is modelled using mixed-integer linear programming approach and is solved via CPLEX® optimizer. Both grid-connected and stand-alone modes of operation are investigated. Despite of high operation cost in island mode, coordination of energy storage systems, incentive-based and price-based demand response (DR) programmes affect economy of microgrids. The framework is examined on a test microgrid. Results show that both of the releasing the microgrid master controller authority and DR resources result in significant saving in operating; especially in emergency conditions such as islanded mode.

Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing

On-site renewable electricity generation represents an attractive option for manufacturing enterprises to deal with time varying electricity prices while reducing their carbon footprint. Production scheduling can be used to take full advantage of the installed renewable energy capacity for electricity cost reduction. A dynamic scheduling approach is proposed to minimize the electricity cost of a flow shop with a grid-integrated wind turbine. Time series models are used to provide updated wind speed and electricity prices as actual data becomes available. The production schedule and energy supply decisions are adjusted based on the new information. The approach is demonstrated using a case study.

Energy- and Labor-aware Production Scheduling for Sustainable Manufacturing: A Case Study on Plastic Bottle Manufacturing

Among the potential roadmaps towards sustainable production, the emerging energy-cost-aware production scheduling philosophy is considered as one promising direction. Therein, sustainability objectives, e.g., minimization of energy consumption/cost of production processes and stabilization of the electricity grid, can be achieved by manufacturing enterprises in a low-cost manner. However, these sustainability goals should be integrated with conventional production constraints besides the due date, e.g., reasonable labor cost based on work shifts, no production at weekends, and changeovers for different product types. This paper formulates a mixed-integer linear programming model for energy- and labor-cost-aware production scheduling at the unit process level, considering all the aforementioned constraints. A state-based energy model is used to reveal the energy consumption behavior of a process over time. It thus enables fine-grained energy-aware production scheduling. A case study is conducted for a blow molding process in a Belgian plastic bottle manufacturer. The measured power data enables to build an empirical energy model. The production scheduling is performed under real-time electricity pricing data. As a result, production loads are automatically shifted to the optimal periods. The optimal idle mode is automatically selected between production loads (powering off, idle, etc.). A schedule of joint energy cost and labor cost minimization is demonstrated to reduce 12% and 5% of total cost, compared to schedules that minimize energy and labor cost, respectively. In conclusion, although the labor wage is usually higher during periods with lower electricity price, energy and labor costs can be jointly optimized as a single objective to help factories minimize the production expenditure.

Strong Formulations for Multistage Stochastic Self-Scheduling Unit Commitment

With the increasing penetration of renewable energy into the power grid system, the volatility of real-time electricity prices increases significantly. This brings challenges for independent power producers to provide optimal bidding strategies. The traditional approaches of only attending the day-ahead market might not be profitable enough without taking advantage of real-time price volatility. In this paper, we study the optimal bidding strategies for the independent power producers utilizing self-scheduling strategies to participate in the real-time market considering real-time electricity price volatility, with the objective of maximizing the total expected profit. Considering the correlations of renewable energy generation outputs among different time periods, the correlations of real-time prices are captured in our modeling framework, in which we explore a multistage stochastic scenario tree to formulate the price uncertainties. Accordingly, the derived multistage stochastic self-scheduling unit commitment problem is transformed as a deterministic equivalent mixed-integer linear programming formulation. To overcome the curse of dimensionality, we develop strong valid inequalities for the derived stochastic unit commitment polytope to speed up the algorithms to solve the problem. In particular, we derive strong valid inequalities that can provide the convex hull descriptions for the two-period case and a special class of the three-period cases with rigorous proofs provided. Furthermore, strong valid inequalities, including facet-defining proofs, for multistage cases are proposed to further strengthen the model. Finally, numerical experiments verify the effectiveness of our derived strong valid inequalities by incorporating them in a branch-and-cut framework.

Bilateral Electricity Trade Between Smart Grids and Green Datacenters: Pricing Models and Performance Evaluation

Datacenter demand response is a promising approach for mitigating operational instability faced by smart grids. It enables significant potentials in peak load shedding and facilitates the incorporation of distributed generation and intermittent energy sources. This paper considers two key aspects toward real-time electricity pricing for eliciting demand response: 1) two-way electricity flow between smart grids and large datacenters with hybrid green generation capabilities and 2) the geo-distributed nature of large cloud systems, and hence the potential competition among smart grids that serve different datacenters of the cloud. We propose a pricing scheme tailored for geo-distributed green datacenters, from a multi-leader (smart grids) single-follower (cloud) game point of view. At the cloud side, in quest for scalability, robustness, and performance, the energy cost minimization problem is solved in a distributed manner, based on the technique of alternating direction method of multipliers. At the smart grid side, a practical equilibrium of the multi-leader single-follower pricing game is desired. To this end, we employ the technique of equilibrium problem with equilibrium constraints and exact linearization, to accurately transform the multi-leader single-follower pricing game, which is non-convex into a mixed integer linear system that can be readily solved. The effectiveness of the proposed solutions is evaluated based on the real datacenter workload traces and the IEEE 14-bus test systems with real generation and demand data.

Optimal Dispatch Scheduling of a Wind-battery-System in German Power Market

Due to the principle of supply and demand, the real-time electricity price in the German spot market varies throughout a single day. Since wind power is non-controllable and partially unpredictable, it is difficult to schedule its power output. Suppliers lose profits by not taking full advantage of the price variation. Moreover, the forecast errors can represent a financial risk, in case of the provision of reserve energy. In this paper, an approach is presented where a battery energy storage system (BESS) is used to make wind power plants (WPPs) scheduled. First, the BESS is used to adjust the dispatch plan of wind power output, in order to exploit price variations beneficially in day-ahead and intraday markets. Second, the BESS is applied to address forecast errors during the real-time operation, in order to balance particularly the expensive forecast errors, which can endanger the stability of the power system. Deviations between forecast and real power output are characterized as expensive forecast errors, if the payment for the deviations is more than 50 €/MWh. In order to realize a multiuse of the battery, a genetic algorithm is employed to optimize the portion of power and energy capacity for the BESS, which participates in above-mentioned different energy market auctions. In contrast, for the daily operation strategy of the BESS, an hourly-discretized linear optimization algorithm is employed. The wind power forecasts for a pool of wind farms with an overall nominal power of 238 MW are generated with WEPROG‘s (WEPROG GmbH, Wetter & Energie PROGnosen, Böblingen, Germany, http://www.weprog.com/.) multi-scheme ensemble prediction system (MSEPS). In addition, the measured data of the wind farm pool were also available. Time-series data of electricity prices for the German/Austrian area are taken from EEX European Energy Exchange AG, Leipzig, Germany, https://www.eex.com/de/. and used partially for the optimization and verification. The results show that, by applying the proposed method, financial benefits are achieved for the wind farm and the cost caused by forecast errors can be decreased.

Hierarchical Coordination of Heterogeneous Flexible Loads

This paper first proposes a simple yet effective load model to characterize a variety of flexible loads. We then present a hierarchical control framework to coordinate their energy consumption. The upper level of the framework optimizes the aggregate power trajectory of the loads via a two-stage stochastic program, which aims to minimize the total energy costs under uncertain real time electricity prices and renewable generation. At the lower level, we design priority-list based scheduling algorithm for each load type to allocate the resulting power trajectory to individual loads. We then present several numerical examples to compare the performance of the framework against three benchmarks including a fully centralized, globally optimal approach. We find that, while the centralized framework is computationally intractable at even modest numbers of loads, the hierarchical framework scales very well to large numbers of loads and results in electricity costs that are comparable to the centralized benchmark.

Real-time electricity pricing of a comprehensive demand response model in smart grids

This paper proposes a real-time interactional pricing scheme to maximize the social welfare of players in real-time demand response program in smart grids. Lagrangian relaxation–based dual decomposition is used to separate the social welfare optimization problem into a retailer's problem along with many consumers' subproblems, and the gradient projection method is adopted to solve them. First, the consumers' subproblems are solved to determine the optimal demand responses to the price announced by the retailer. To obtain the optimal demand response, a comprehensive mathematical function is developed on the basis of a combination of 5 costumer's utility functions reported in literature (ie, linear, potential, logarithmic, exponential, and hyperbolic). Afterward, the retailer calculates a real-time price in response to the consumers' reactions to maximize its profit. In terms of practical implementation, the consumers and the retailer interact with each other via a limited number of control messages exchanges to find the optimal solution at each hour. The proposed method is evaluated considering the various retailer's cost functions and the consumers' behaviors. Also, the results of elasticity sensitivity analysis are presented from the retailer and consumer viewpoints.