Real-time Electricity Price Research Articles

ADPA Optimization for Real-Time Energy Management Using Deep Learning

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Optimizing domestic energy management with a wild Mice colony-inspired algorithm: Enhancing efficiency and coordination in smart grids through dynamic distributed energy storage

Energy management (EM) is a critical strategy that spans the production and consumption of electricity, enhancing the stability of the electricity network. Smart grid technology significantly improves the electrical system's energy efficiency (EE), facilitating a transformation from a conventional power grid (PG) to a smart PG. This paper presents a key strategy for modeling the EE of the smart grid tailored to domestic demand, establishing smart coordination between domestic demand, energy production, and storage to reduce energy waste and costs. Our model integrates various energy sources, including renewable energy (RE), photovoltaic (PV) systems, wind power, and an energy storage system (ESS), interconnected with the PG. The model's structure ensures the coordinated flow of electricity in a residential house through an optimal control method (OCM). To develop a robust closed-loop control model, we employ Demand Response (DR) schemes within the Real-Time Electricity Pricing (RTEP) framework. We construct a dynamic model of the ESS to compute the System Performance Index (SPI), corresponding to energy costs. To enhance our model, we introduce a Dynamic Distributed Energy Storage Strategy (DDESS). Additionally, we introduce a novel optimization algorithm inspired by the behavioral patterns of wild mice, called the Wild Mice Colony (WMC). By analyzing the targeted and advantageous behaviors of wild mice in colonies, we propose that these behaviors can serve as a model for addressing complex, uncertain problems. This strategy is highly advantageous, capable of reducing total energy consumption (EC) from the main grid by over 100 % of the load demand, optimizing the energy system, and ensuring synchronization. The performance of DDESS optimizes energy flow (EF) during the repayment plan, leading to minimized EC costs from the PG.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

Economic perspectives on renewable energy in offshore oil and gas development: Integrating multiple functions combination degradation maintenance strategies

The development and utilization of renewable energy has become a research hotspot in recent years, and it has begun to be experimentally applied in the development of “ultra-deep water” oil and gas fields. This paper proposes an economic perspective of renewable energy in offshore oil and gas development, evaluating the economic feasibility of large, complex, and multi-equipment systems, as well as the grouping maintenance of the system with multiple types of reliability functions. Comprehensively considering the construction cost, maintenance cost and real-time electricity price cost, the reliability and maintenance cost are the objective function, and the real-time electricity price is the influencing factor. The operating costs of offshore oil and gas development over long distances and offshore wind energy are compared. The results show that grouping maintenance can greatly reduce the total cost. As an alternative energy solution, offshore wind can meet the demand for clean energy in the context of economic growth, although the total cost is higher than that of long-distance transmission.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

ATTnet: An explainable gated recurrent unit neural network for high frequency electricity price forecasting

The primary contribution of this study is the proposal of an explainable deep-learning neural network (ATTnet) that employs an attention mechanism to achieve accurate electricity spot price forecasting and an explainable model pipeline. The concise, single-stream network consists of a 5-head attention mechanism and gated recurrent units, which have been developed to model the temporal dependencies of the volatile market data. In addition to introducing a novel neural network architecture for volatile time series data, this study makes a substantial contribution by investigating prediction factors in two ways: temporally via the attention scores from the input sequences and globally via feature Shapely values. In real-time electricity price prediction, historical prices, temperature, hour, and zonal load are found to be the most important variables. The deep learning model was tested on real-time price profiles from eight generators within the New York Independent System Operator (NYISO) network. The proposed model achieves performance gains of 21% in MAE and 22% in MAPE over the state-of-the-art benchmark methods.

Incorporating System Frequency Dynamics Into Real-Time Locational Marginal Pricing of Electricity

Incorporating System Frequency Dynamics Into Real-Time Locational Marginal Pricing of Electricity

Customer-oriented scheduling of active distribution system considering integrated demand response programs and multi-carrier energy hubs

The increasing development of infrastructure and improvement of automation services have changed the role of household loads from fixed and unresponsive loads to responsive household loads. Meanwhile, the demand response program capability of smart residential consumers, considering their comfort level along with their power exchange effects on energy hubs, is an important part of active distribution system Scheduling, which should be noted comprehensively. In this regard, this paper presents a two-stage optimization framework based on iteration for customer-oriented scheduling of an active distribution system by incorporating the participation of smart residential consumers in energy hubs. In the first stage-first level, a flexible integrated demand response program process along with a storage system for smart residential consumers with real-time electricity prices is implemented to maximize the comfort level of smart residential consumers. In the first stage-second level, the energy hub operator presents day-ahead optimal self-scheduling to maximize their profit and send their bid strategies to the distribution system operator, considering the effects of smart residential consumer's power exchange on profit and cost. Finally, in the second stage of the optimization model, customer-oriented scheduling of an active distribution system is implemented by the distribution system operator to minimize operating costs. The comfort level of smart residential consumers for 20, 25, and 30 households of the first, second, and third energy hubs are increased by 112.92%, 161.64%, and 120.77%, respectively, compared to the base case. In addition, the total operating costs of active distribution system and energy hubs are reduced by $38,200, $4643, $3287, and $5523, respectively, compared to the base case.

A Hybrid Real-Time Electricity Pricing Strategy with Carbon Capture, Storage and Trading

During the power system low carbonization process, various policies and technologies for low-carbon have been rapidly developed. Real-time demand side management by energy suppliers based on real-time pricing (RTP) has become a reality via smart meter technology. Motivated by this, an RTP mechanism is used to guide users’ demand response, and the carbon trading regulation (CTR) and carbon capture, storage (CCS) technology are used on the supply side. Through these means, we can reduce carbon emissions from the power system and achieve the goal of carbon neutrality. Furthermore, a game model is used to construct the relationship between the energy supplier and users. Since the energy supplier acts first and then the user make their decision, the leader is the energy supplier and the followers are users. Further, to protect the information privacy of them and independently solve the goals of them, a distributed solution approach combining a differential evolutionary (DE) algorithm and Gurobi solver is developed for finding the equilibrium solution. The proposed model’s economic and environmental benefits are verified and the effectiveness of CCS technology and CTR is accessed via the numerical analysis, what means the proposed strategy can serve as a guide for the system’s low-carbon management.

Controlling the energy supply and demand of grid-connected building integrated photovoltaics considering real-time electricity prices to develop more sustainable and smarter cities

Limited resources and energy supply constraints in today's society necessitate innovative solutions based on existing potentials and the possibility of development in the upcoming years. From this perspective, to efficiently manage both the supply and demand sides of energy in buildings, this study proposes a novel approach for grid-connected building integrated photovoltaics (BIPVs) considering the price of electricity. Regarding real-time electricity prices, the presented method allows for selecting grid electricity or photovoltaics per peak, off-peak, and between these two. In addition to reducing costs, this strategy lessens photovoltaic installation capacity and increases the justification of BIPVs. Considering demand-side management, proper control in terms of economic aspects is conducted based on the system's defined model and the price of electricity. The suggested technique outperforms other applied methods when both energy supply and demand are considered, as well as real-time electricity prices. The validity of this technique is confirmed by simulation results of system performance in a setting and for lighting loads. This approach advocates for establishing smart buildings and cities in tandem with the growth of smart grids and liberalized energy markets.

Unleashing the economic and ecological potential of energy flexibility: Attractiveness of real-time electricity tariffs in energy crises

The European energy crisis in 2021 and 2022 emphasized the importance of energy flexibility to mitigate price peaks and manage increased market volatility. Dynamic electricity tariffs are key to unlocking the potential of energy flexibility, as they incentivize flexible consumers to reduce their costs by shifting their load to periods of low prices. We quantify the potential of dynamic tariffs and focus on their economic and ecological potential particularly in energy crises. Using German Day-Ahead spot market data covering 2019 to 2022 as basis for a dynamic tariff, we determine the cost and emission spread between non-flexible and flexible industrial processes. Our results show that energy flexibility together with the real-time electricity tariff lead to energy cost reductions, with relative cost reductions of flexible loads being up to 12 times higher in the energy crisis. Moreover, pre-crisis electricity costs and associated emissions were highly positively correlated, implying flexibilities in real-time electricity tariffs may minimize electricity costs while simultaneously reducing emissions. Based on our results, we conclude that real-time electricity pricing provides a suitable instrument to (1) incentivize necessary investments in energy flexibility, especially in energy crises, and (2) facilitate flexible consumers to reduce costs and emissions at the same time.

Intelligent Building Energy Management Considering Uncertainties and Variation of Day-Ahead and Real-Time Electricity Prices

Intelligent Building Energy Management Considering Uncertainties and Variation of Day-Ahead and Real-Time Electricity Prices

Attention Mechanism Multi-size Depthwise Convolutional Long Short-term Memory Neural Network for Forecasting Real-time Electricity Prices

Attention Mechanism Multi-size Depthwise Convolutional Long Short-term Memory Neural Network for Forecasting Real-time Electricity Prices

A novel energy management method for multiple residential energy systems with energy exchange

Energy saving and emission reduction are the worldwide outstanding problems in the development of economy and society, which lead to wide attention of residential energy management. This paper presents a novel energy management method for multiple residential energy systems with energy exchange based on action-dependent heuristic dynamic programming (ADHDP). The major contributions are displayed as follows: (1) a novel energy management model is established for multiple residential energy systems with real-time electricity price and time-varying residential loads; (2) a novel ADHDP based energy management method is presented to obtain the energy scheduling scheme of multiple residential energy systems; (3) the continuous control action of the overall system is transformed into discrete control space which avoids training the action network. First, the structure and system models of multiple residential energy systems are introduced, and the energy management problem is formulated. Then, a brief introduction of ADHDP method is provided. Based on this, an energy management method is presented and an effective discretization method of control action is developed for solving the presented energy management problem with less computational burden. Simulation results show that the presented method can reduce the energy cost effectively. Finally, the comparison results verify the superiorities over the traditional energy management methods.

A smart predict-and-optimize framework for microgrid's bidding strategy in a day-ahead electricity market

This paper deals with the microgrid's bidding strategy (MGBS) problem in a day-ahead (DA) electricity market. To this end, the DA electricity prices, demand, and renewable energy uncertainties are modeled by different scenarios via generating several representative daily curves. However, the real-time (RT) electricity prices are modeled using robust optimization (RO) via predicting appropriate intervals. The existing paradigm to predict the RT intervals is to minimize the statistical errors, while this accuracy-oriented method may not necessarily yield the best MGBS plan against the actual realization of RT prices. The novelty of this research is to present a smart predict-and-optimize (SPO) methodology in which a cost-oriented prediction model replaces the existing accuracy-oriented methods. Trilevel mathematical programming is established to construct a cost-oriented model where the first level maximizes/minimizes the actual profit/cost by adjusting the predicted interval bounds of the RT prices. Knowing these trained interval bounds, the second level runs a scenario-based DA-MGBS. Then, a rescheduling problem is solved at the third level based on the actual realization of uncertainties. To solve this trilevel problem, a reformulation-and-decomposition (R&D) algorithm is utilized. The numerical experiment illustrates the advantages of the SPO method in terms of the microgrid (MG) cost.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method.