Real-time Electricity Price Research Articles

Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

Demand response scheduling algorithm of the economic energy consumption in buildings for considering comfortable working time and user target price

Demand response is an important way to improve buildings energy consumption.It aims to schedule the various appliances in the buildings to let their energy consumption match the real-time electricity price provided by smart grid. Based on buildings energy management systems, a DR scheduling algorithm is presented for buildings economic energy consumption. It defines the user target price to indicate different user requirements and learns the working time of the appliances for the residents' comfort in the buildings by logistic regression. Thus, the presented algorithm dynamically adjusts the maximum power threshold, and then automatically arranges the operation of the appliances to achieve buildings economic energy consumption. Simulation results show that the presented algorithm can significantly reduce the energy consumption demand during peak price period while ensuring the residents’ comfort, and the total energy cost can be reduced by 15.1% compared with the user's expectation. © 2021 Elsevier Science. All rights reserved.

Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets

The ability to forecast real-time electricity price for wind power is key to the operation of energy markets and hedging price risks. Recent research suggests new deep neural network (DNN) architectures can capture temporal dependencies in historical price data, along with the ability to automatically extract important features of the dataset. However, most existing price prediction DNN representations still utilize basic architecture designs and either no pre-training, or simple training approaches. This work studies both the effect of transfer learning on three network representations and different source domains, as well as the mechanism of transfer learning. It is shown that transfer learning improves accuracy across all network representations. The best performance is obtained with a GRU-based architecture, termed GRU-TL, that has been pre-trained from a hybrid dataset of all wind farms in the same subzone. This model outperforms all statistical and deep learning benchmarks by an average of 6.7% in the mean absolute percent error (MAPE) metric. The underlying mechanism of transfer learning enables the pre-trained DNN representation to learn the features of the target dataset more accurately.

How would real-time electricity pricing affect the Saudi power sector in the long-run?

This paper shows the effects of real-time electricity pricing (RTP) on the long-run marginal costs of power generation in Saudi Arabia. To do this, it links a multi-sector energy system model with a residential electricity use model. The energy system model contains an economic power dispatch optimization component. The residential framework embeds households, whose decisions are governed by microeconomic principles, in a physical building energy model. The analysis entails liberalizing fuel prices for the power utilities and setting the dynamic prices of electricity equal to the long-run marginal electricity supply costs. The electricity prices are solely offered to households. The key takeaways from this analysis are:•RTP, a form of dynamic electricity pricing, reduces the variability of the marginal costs for Saudi power utilities throughout the day.•Lowered capital spending by the Saudi power sector results from RTP and consequently lower power loads. Moreover, the curtailed investment in power plants would more than cover the costs of residential smart meter replacements.

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

Estimating the Elasticity to Real-Time Pricing: Evidence from the Spanish Electricity Market

We estimate consumer response to the first large-scale real-time electricity pricing (RTP) program. In 2015, RTP became the default tariff for residential customers in Spain, exposing them to prices that vary hourly based on day-ahead wholesale prices. Using expected national wind production as an instrument for price, we estimate household-level demand elasticities for RTP households and non-RTP (placebo) households. Estimates show no difference in behavior across RTP and non-RTP households. Reasons for nonresponse may include low potential gains or high nonmonetary costs of information acquisition and behavioral change. We discuss implications for optimal dynamic electricity pricing.

Construction of automatic demand response dynamic model for distributed new energy consumption

Based on the current situation of demand side management in China, considering the output characteristics of new energy, this paper analyzes various influencing factors of automatic demand response from the aspects of user response mode, power consumption characteristics, response time, load economy, etc. Taking energy storage air-conditioning users as an example, the paper uses real-time electricity price as demand response incentive measure for demand regulation, and verifies the implementation effect of the optimal response strategy of electricity price incentive.

A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mixed-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Bilevel energy optimization for grid-connected AC multimicrogrids

As a key component of an alternating current (AC) multimicrogrid (MMG), an energy management system must gather the necessary information and coordinate the corresponding equipment to optimize the economic benefits. To achieve this goal, this paper proposes a bilevel energy optimization model. The lower level model considers the real-time electricity price of distribution networks (DNs) to optimize the operation cost of each grid-connected submicrogrid (SMG). The upper level constructs an MMG optimization model based on graph theory, which optimizes the energy transmission path among the SMGs by considering the transmission losses. In this manner, the upper level model can further reduce the economic cost of an MMG by energy trading among the SMGs. To solve this model, a nested Jaya–Dijkstra algorithm is customized. Moreover, a Jaya algorithm with nonlinear variation capability (NVCJA) is developed to avoid the premature convergence problem and enhance the solution precision. The performance of the bilevel optimization strategy is evaluated considering a typical AC MMG system. For the proposed NVCJA, the convergence time is 24.07%, 39.47%, 37.76%, and 48.77% less, and the standard deviation is 12.97%, 28.71%, 23.89% and 35.92% lower than those for the Jaya, genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms, respectively. The analysis results show that the proposed optimization algorithm exhibits a high operating speed and stability. The economic cost of the proposed bilevel optimization model is 19% and 29% lower than those of the hierarchical optimization and linear relaxation optimization models, respectively. The test results show that the proposed optimization model can effectively enhance the economic benefits of an MMG.

A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup

Dynamic pricing with automation and option to sell in-house-generated renewable energy to the grid attract consumers to the future retail electricity market. Real-time pricing of electricity in the Indian context has not been relevant so far because the pricing in Indian urban retail electricity market is mostly restricted to block-rate pricing. This study describes a novel method for segmenting the retail electricity market in a university setup. The experiment was focused on differential pricing, and the data were collected from 173 respondents using a questionnaire. This study prescribes different criteria for classifying a consumer into the specified market segments using cluster analysis and discriminant analysis. The demand–price relationship and price elasticity are studied for each segment. Demand characteristics of four appliances are also discussed. The study found that in some cases, consumers’ willingness to pay is about five times higher than the present average price.

Real-Time Energy Management for Microgrid With EV Station and CHP Generation

A microgrid scenario that consists of electric vehicle (EV) station, combined heat and power (CHP) co-generation, main power grid, and external natural gas station, as well as thermal energy storage water tank is considered in this paper. By jointly considering the uncertainty of real-time electricity price, electricity and heat demands and EVs' behaviours, the EVs' batteries are used as energy storage adjusters to reduce the time-averaged operation cost, which includes energy acquiring cost from main power grid, the natural gas cost for CHP generation and heat only generation. We propose a real-time energy management algorithm for EV based microgrid by adopting Lyapunov stochastic optimization technique which optimizes the microgrid average cost without knowing future price, demands and other system information. Furthermore, our approach can accommodate a wide spectrum of characteristics of EVs, including the independent charging/discharging action, dynamic charging energy limits, and lifetime-guaranteed battery. Lastly, extensive simulations are provided to demonstrate that our proposed algorithm can outperform the other three alternatives.

Spot trading strategy considering multiple constraints for electricity retailers

In order to solve the inert trading and irrational trading of electricity retailers in the spot market trading environment, increase the income of electricity retailers and reduce transaction risk, a spot trading strategy considering multiple constraints for electricity retailers is proposed. Similar days are selected by analyzing the date type and the temperature difference of the spot market trading day, on the basis of similar days, the electricity consumption information of electricity retailers’ agent users, day-ahead and real-time electricity price of sport trading market are predicted, combing the user-side excess profit recovery fee, mid-to-long term shortage recovery fee and mid-to-long term curve deviation recovery fee restrictions in Shanxi Province spot trading rules, optimize the mid-to-long term daily 96 points daily electricity purchase curve and day-ahead 96 points daily electricity biding curve, and use the real data of Shanxi spot market transactions to calculate the multi-day income of the electricity retailers to verify the practicability of the spot trading strategies.

Operational optimization of a building-level integrated energy system considering additional potential benefits of energy storage

As a key component of an integrated energy system (IES), energy storage can effectively alleviate the problem of the times between energy production and consumption. Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems. This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system (BIES) considering additional potential benefits of energy storage. Based on the characteristics of peak-shaving and valley-filling of energy storage, and further consideration of the changes in the system’s load and real-time electricity price, a model of additional potential benefits of energy storage is developed. Aiming at the lowest total operating cost, a bi-level optimal operational model for day-ahead operation of BIES is developed. A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation. The results demonstrate that the model can exploit energy storage’s potential, further optimize the power output of BIES and reduce the economic cost.

Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study

Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through the retail electricity provider (REP), who has more naturally aligned incentives to avoid high wholesale electricity prices and maintain customer satisfaction, as compared to distribution utilities, grid operators, and the average residential consumer. Retail electricity providers who serve residential consumers are exposed to substantial price risk as they generally have a portion of their portfolio exposed to variable real-time wholesale electricity prices, despite charging their residential customers a fixed retail electricity price. Using Monte Carlo simulations, we demonstrate that demand response, executed through internet-connected thermostats, to shift real-time residential HVAC load in response to real-time prices, can be used as an effective physical hedge, which is both less costly and more effective than relying solely on financial hedging mechanisms. We find that on average a REP can avoid USD 62.07 annually per household using a load-shifting program. Given that REPs operate in a low margin industry, an annual avoided cost of this magnitude is not trivial.

A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price

With the innovation of power market and the development of energy intelligent technology, load forecasting technology as an important direction of power system development plays an important role in power system planning. Aiming at the problem of insufficient feature extraction and low prediction accuracy, a short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price is proposed in this paper. Firstly, the maximum information coefficient method is used to analyze the correlation between electricity price and load. The historical load, real-time electricity price, weather and other factors are constructed in the form of continuous feature maps as input. Secondly, the Convolutional Neural Network (CNN) is used to cascade the shallower and deeper feature from four different scales. Feature vectors of different scales are fused as the input of Long Short Term Memory (LSTM) network , and LSTM network is used for short-term load forecasting. Finally, the proposed method is used to predict the real load data of a city in Liaoning Province. The experimental results show that the proposed method has higher prediction accuracy than the standard LSTM model, Support Vector Machine (SVM) model, Random Forest (RF) model and Auto Regressive Integrated Moving Average (ARIMA) model. Besides, the prediction results show that this study has high application value and provides a new way for the development of power load forecasting.

Locational marginal price forecasting in a day-ahead power market using spatiotemporal deep learning network

Abstract Locational marginal pricing (LMP) in a deregulated electricity market is a result of bidding competition from nodes (locations). This paper presents a deep learning neural network for the 24 h-ahead LMP forecasting of a specific node in a zone at the PJM power market using other zonal prices. The presented method uses a Convolutional Neural Network (CNN), which is a well-known architecture for deep learning that has been applied successfully for image classification and pattern recognition. The CNN is used herein to extract features of real-time electricity prices by convolution, kernel, and pooling operations using spatiotemporal electricity prices in various zones and locations. The optimal parameters of the model were determined using a step-by-step manual approach to minimize the burden of brute force trial and error methods. Datasets from the PJM power market are used in simulations in which the proposed method is applied using Tensorflow and Keras Library. Various approaches are compared, and the results thus obtained demonstrate that the proposed method has higher accuracy than traditional methods in 24 h-ahead LMP forecasting.

Constrained Thompson Sampling for Real-Time Electricity Pricing With Grid Reliability Constraints

We consider the problem of an electricity aggregator attempting to learn customers' electricity usage models while implementing a load shaping program by means of broadcasting dispatch signals in real-time. We adopt a multi-armed bandit problem formulation to account for the stochastic and unknown nature of customers' responses to dispatch signals. We propose a constrained Thompson sampling heuristic, Con-TS-RTP, as a solution to the load shaping problem of the electricity aggregator attempting to influence customers' usage to match various desired demand profiles (i.e., to reduce demand at peak hours, integrate more intermittent renewable generation, track a desired daily load profile, etc). The proposed Con-TS-RTP heuristic accounts for day-varying target load profiles (i.e., multiple target load profiles reflecting renewable forecasts and desired demand patterns) and takes into account the operational constraints of a distribution system to ensure that customers receive adequate service and to avoid potential grid failures. We provide a discussion on the regret bounds for our algorithm as well as a discussion on the operational reliability of the distribution system's constraints being upheld throughout the learning process.

Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast

This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.

Study on Optimal Dispatching Strategy of Regional Energy Microgrid

Considering the fluctuation of microgrid output and customer’s demand, an optimal dispatching strategy for the combined cooling, heating, and power supply microgrid is proposed. The fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics, and stability in combined cooling, heating, and power microgrid operation. Therefore, the extreme learning machine optimized by particle swarm algorithm is used to improve the prediction accuracy of photovoltaic power generation, wind power generation, and load power. The regularization coefficient C and the kernel parameter λ of kernel extreme learning machine are regarded as the optimization targets of the particle swarm algorithm so that the prediction accuracy can be improved. Forecasted value of cooling, heating, and electricity microgrid system and new energy power generation as well as real-time electricity price, fuel unit price, etc. are considered in the operating cost. In order to minimize the operating cost and improve the energy utilization, an improved shuffled frog leaping algorithm is used to solve the cost minimization problem to give the equipment output dispatch strategy. Comparative simulation results can be found that under the same conditions, compared to the kernel extreme learning machine and the kernel extreme learning machine optimized by the genetic algorithm, the kernel extreme learning machine optimized by the particle swarm has faster convergence speed and higher prediction accuracy. Comparative simulations of microgrid dispatching on typical days in summer and winter are carried out. Compared with the cost of distribution, the cooling, heating, and power microgrid based on the improved shuffled frog leaping algorithm has obvious economic benefits and higher energy utilization property.

Price-sensitive home energy management method based on Pareto optimisation

ABSTRACT With the emergence of smart home, residents can achieve demand response through home energy management system (HEMS), so as to improve energy utilisation. However, on the one hand, the existing household energy management method lacks the balance consideration of user economy and comfort, on the other hand, it lacks the response of adjustment scale to real-time electricity price. Aprice sensitive response mechanism considering the comfort of HVAC is added to the objective function of household energy management, and aprice sensitive household energy control method based on Pareto optimisation is proposed, which is solved dynamically by mixed integer linear programming (MILP). The simulation results show that PSHEMS can reduce household daily expenditure by 53%, and the PAPR value of home nano grid can be reduced by 70%.