Real-time Electricity Price Research Articles

A nuclear‑hydrogen hybrid energy system with large-scale storage: A study in optimal dispatch and economic performance in a real-world market

A study in optimal dispatch and economic analysis of a novel nuclear hybrid energy system (NHES) with large-scale hydrogen storage and a novel control scheme is conducted. The control scheme makes charge and discharge decisions by using the real-time electricity price relative to the historical price distribution. The pricing data is taken from the ERCOT Houston and West load zones with different price oscillations. Six case control studies with different levels of aggressiveness were chosen for each load zone for a total of 12 case studies. It is found that in a high price volatility load zone, a more aggressive control strategy maximizes revenue, while in a less oscillatory load zone, a moderate control strategy performs better. The NHES can store energy when the price is low and sell additional electricity when the price is high, as well as participate in the ancillary services market. Due to these advantages, the NHES can generate 10–40 million USD more revenue annually than a stand-alone nuclear plant. Due to higher costs, the NHES has a higher LCOE (81.36 USD/MWh) than a stand-alone plant (68.66 USD/MWh); however, the LCOE of the NHES is still comparable to other new forms of electricity generation. It is found that both the NHES and the stand-alone plant are not economical in the existing electricity market. Due to the extra revenue generated, the hybrid system has a positive annual cashflow whereas the stand-alone plant does not. It is found that the hybrid plant needs less cost subsidy than a stand-alone plant to become economical.

QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting

Real-time electricity market data is highly volatile and very noisy. The properties of such data make forecasting models difficult to develop, with traditional statistical models in particular affected by the “curse of dimensionality” for such data. However, autoencoders, or neural networks specifically designed to reduce the noise and dimensions of input data, may prove useful to advance the accuracy of real-time price forecasting models. This paper studies the optimal design of such an autoencoder, developing a quadruple branch, CNN-based autoencoder (QCAE) which is pre-trained and then directly linked to a forecasting model. The QCAE compresses the input data in both time and feature directions. Ablation analyses verify the architecture of the QCAE, and its integration with the forecasting model is tested and validated on fifty generators in the New York Independent System Operator (NYISO) power grid. The QCAE forecasting framework outperforms benchmark and state-of-the-art models with an average improvement of 6.3% in sMAPE and 3.10% in MAE.

Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning

With the increasing penetration of electric vehicles (EVs), the orderly charging–discharging (C–D) strategy of EVs can effectively alleviate the impact of large-scale grid-connected EVs. However, the schedule of the C–D system of EV clusters encounters the curse of dimensionality problem. In addition, the performance of the C–D control strategy faces great challenges of environmental uncertainty of user demand and electricity price. This paper proposes an EV cluster scheduling strategy considering real-time electricity prices based on deep reinforcement learning. Firstly, we establish a distributed real-time optimal scheduling structure according to the real-time price signals of distribution system operators (DSO). Furthermore, to alleviate the curse of dimensionality, we propose a C–D model of a single EV according to the C–D characteristics of EV, and we establish the C–D control model of EVs as a Markov decision process (MDP). Finally, to adapt to the uncertainty of the learning environment, we propose a model-based deep reinforcement learning to optimize the C–D behavior of EVs. After day-ahead training and parameter saving of the proposed model, the C–D scheduling strategy is generated for the real-time system state at each moment of the day. The simulation results of the C–D scheduling strategy for cost-oriented EV charging show that the proposed scheduling strategy effectively reduces the user charging cost by 133.7 dollars and the load peak–valley difference, stabilizes the load fluctuation, and achieves the win–win situation between the power grid and EV users.

Deep Learning with Multisource Data Fusion in Electricity Internet of Things for Electricity Price Forecast

More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.

Optimal Energy Management for Multi-Microgrid Under a Transactive Energy Framework With Distributionally Robust Optimization

The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

Analysis of Microgrid’s Operation Integrated to Renewable Energy and Electric Vehicles in View of Multiple Demand Response Programs

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.

Optimal Planning of Residential Microgrids Based on Multiple Demand Response Programs Using ABC Algorithm

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.

A distributed real-time pricing strategy based on reinforcement learning approach for smart grid

The real-time pricing (RTP) plays an important role in demand side management of smart grid. In this paper, a distributed RTP strategy which takes the interests of both supply and demand sides into consideration is studied. A holistic model focusing on the interactions between users and the power supplier is proposed in the framework of Markov decision process (MDP). The MDP presentation of smart appliances’ operational processes well embodies their characteristics and the energy correlation of adjacent time slots. Different from existing ones, a novel distributed online algorithm based on a reinforcement learning approach is proposed to solve the MDP model without acquisition of the transition probabilities. Through information exchange between users and the power supplier, the real-time electricity price is decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. The specific information of the utility function for each user does not need to be disclosed to the supplier and other users. Simulation results show that the proposed model and algorithm balance energy supply and demand well, and have a good performance in peak shaving and valley filling.

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%.

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.

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks.

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

Dispatch Strategies for the Utilisation of Battery Storage Systems in Smart Grid Optimised Buildings

This study investigates Smart Grid Optimised Buildings (SGOBs) which can respond to real-time electricity prices by utilising battery storage systems (BSS). Different building design characteristics are assessed to evaluate the impact on energy use, the interaction with the battery, and potential for peak load shifting. Two extreme cases based on minimum and maximum annual energy consumption were selected for further investigation to assess their capability of utilising BSS to perform arbitrage, under real-time pricing. Three operational dispatch strategies were modelled to allow buildings to provide such services. The most energy-efficient building was capable of shifting a higher percentage of its peak loads and export more electricity, when this is allowed. When using the biggest battery (220 kWh) to only meet the building loads, the energy-efficient building was able to shift 39.68% of its original peak loads in comparison to the 33.95% of the least efficient building. With exports allowed, the shifting percentages went down to 31.76% and 29.46%, respectively, while exports of 18.08 and 16.34 kWh/m2 took place. The formation of a regulatory framework is vital in order to establish proper motives for buildings to undertake an active role in the smart grid.

A fuzzy coordinated energy management strategy for energy storage units in DC multiport energy router

As the proportion of new energy in power systems continues to increase, the widespread use of solar and wind-based distributed generation (DG) power in DC distribution networks will face problems such as new energy consumption, grid stability, and economical benefits in the future. At present, energy management strategies for photovoltaic (PV) grid-connected power generation systems are mainly researched in the field of AC–DC distribution network. This paper provides an advanced energy management strategy of the DC multiport energy router. Considering the battery state of charge (SOC), real-time electricity price (EP), and user-side electricity charge (EC), an energy management scheme based on the fuzzy coordinated control strategy is designed to improve the utilization of energy storage batteries and the economic benefits. A 20 kVA multi-port DC energy router simulation platform is established and verified in MATLAB/Simulink. The results show that the energy management strategies based on fuzzy coordinated controller can solve the multi-objective problem. Compared with traditional control strategies, the fuzzy collaborative energy management strategy has a more comprehensive optimization effect.

GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting

A highly accurate electricity price prediction model is of the utmost importance for multiple power systems tasks, such as generation dispatch and bidding. Due to the liberalization of the electricity market, as well as high renewable penetration, the properties of electricity price time series are becoming more stochastic and complex. Traditional statistical methods and machine learning algorithms cannot model such volatile market conditions with high fidelity. In this paper, we propose a data-driven deep learning network (GHTnet) to capture the temporal distribution of real-time price data. A new CNN module, based on GoogLeNet, is developed to capture the high-frequency features of this data, while inclusion of time series summary statistics is shown to improve the forecasting of volatile price spikes. The deep learning model is developed and validated on real-time price time series from 49 generators in the New York Independent System Operator (NYISO), achieving significant performance improvements over that of state-of-the-art benchmark methods, with an average 17.34% improvement in MAPE.

A cosh-based smoothing Newton algorithm for the real-time pricing problem in smart grid

In a smart grid system, different types of electricity users such as residential, commercial and industrial users have different utility functions. This paper proposes a nonlinear constrained optimization model for real-time pricing that maximizes the total utilities of all the different user groups. A Karush–Kuhn–Tucker equation system is employed to solve the model. However, it is a challenging task to solve the system due to the nonlinear complementary condition. To tackle the challenge, a cosh-based smoothing approximation function is proposed to substitute the nonlinear complementary condition. Subsequently, the smoothing Newton algorithm is developed to solve the new equation system. The global convergence and local quadratic convergence of the algorithm are proved. Numerical experiments are performed to test the smoothing method and compare the solutions of real-time pricing, fixed pricing and time-of-use pricing strategies applied in the smart grid system. The results show that the real-time pricing mechanism is the most suitable in saving energy and reducing peaks and troughs in energy consumption. This also indicates that it is effective to use the smoothing Newton algorithm to solve the problem of real-time electricity pricing for smart grid. We obtain that the smoothing approximation function is effective for the supply–demand balance constraints in smart grid.

Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach

With the penetration of intermittent renewable energy sources, greater uncertainty has been brought to the power generation system, creating increased challenges to real-time pricing (RTP). Different from the existing studies, this paper aims to design an RTP strategy for the smart grid which integrates multi-energy generation on the supply side. Without loss of generality, small-scale distributed energy generation and power storage devices for users are also considered. Taking the interests of both supply and demand sides into consideration, a bilevel stochastic model for real-time demand response in the framework of Markov decision process (MDP) is formulated. The model well captures the interactive characters of both sides. Regarding the difficulty of collecting exact information from users in a centralized way in practice, a novel distributed online multi-agent reinforcement learning algorithm is proposed to solve the MDP model without acquisition of the transition probabilities. Through the information interaction between the upper and lower levels, the real-time electricity prices are decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. Simulation results demonstrate that the proposed pricing method and algorithm have a good performance in cutting peak and filling the valley and guarantee the benefits of both supply and demand.

Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study

The rapid development of battery energy storage technology provides a potential way to solve the grid stability problem caused by the large-scale construction of nuclear power. Based on the case of Hainan, this study analyses the economic feasibility for the joint operation of battery energy storage and nuclear power for peak shaving, and provides an effective solution framework for construction scale and battery type determination. Under the proposed framework, a novel cost model for the large-scale battery energy storage power station is proposed. Then, economic analysis is conducted to get the most economical battery type and construction scale by considering the comprehensive economic benefit of the joint operation under the limit of the load factor. On this basis, sensitivity analysis of economic indicators to control parameters and economic parameters is performed to further demonstrate the economic feasibility of the joint operation. Comparative analysis shows that 270 MW lithium iron phosphate battery energy storage power station has the best and stable comprehensive performance in terms of the IRR, PBP and LCOE, which are 16.27%, 6.27 year and 0.464¥/kWh, respectively. In comparison with the pumped storage, the battery energy storage has lower initial investment, faster capital recovery and smaller floor area under the joint operation mode. Moreover, sensitivity analysis illustrates that the large-scale application of battery energy storage still depends on the trade-off between the cost performance of battery and the real-time electricity price.

Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty

Recent years have witnessed a growing trend in the participation of renewable energy on the generation side and relatively high peak loads on the demand side, which makes it gradually challenging for traditional methods concentrating separately on the generation side to maintain system balance. Load resources, with potentialities to provide faster and more economical responses to balance signals, are able to make contributions to system balance through demand response(DR) programs in addition. With the reformation and development of the electricity market, load aggregators(LAs) pear as representatives of small-scale customers and generations to meet response limitations and participate in DR programs. The LA in this paper, which aggregates residential customers and a PV system with battery energy storage(BES) units, balances the power by optimal scheduling and bidding in Day-ahead(DA) and real-time(RT) markets based on real-time electricity price(RTP). And the objective of this paper is to maximize the profits of LA. A majority of papers use deterministic methods for the modeling of renewable generations and residential loads. However, influenced by multiple factors, the actual renewable outputs and residential responsive loads towards real-time electricity prices are unavoidably uncertain. These uncertainties bring risks to LA’s scheduling and bidding strategies, which results in the reduction of LA’s profits. A stochastic model based on the scenario generation method is adopted to reflect the uncertainties of customers’ responsive loads and the PV system’s outputs. The objective profit function turns out to be a risk function influenced by uncertain factors and the risk control method conditional risk at value(CVaR) is integrated to obtain optimal solutions for this maximization problem. Case studies have verified the effectiveness of the proposed strategy.

Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems

This paper proposes a novel decentralised control approach dedicated to coordinating the operation of a large population of residential thermal energy storage characterised by diversified specifications, while innovatively considering the operational constraints on both the national and the local level. The presented iterative algorithm sequentially updates the storage operational schedule under the real-time electricity price scheme to achieve cost savings for each individual participant and the total system, while effectively avoiding the violation of local distribution network restrictions. The transmission topology is explicitly considered to investigate the impacts of transmission congestion on the electricity marginal prices. The results of a series of case studies demonstrate that the proposed coordination approach can effectively enable individual energy arbitrage and achieve 22.53 % system cost savings compared to the 10.34 % savings when coordination is absent. Moreover, the simulation results manifest that the coordinated control approach can cost-effectively perform distribution congestion management to ensure no local network is overloaded, which will cause 12.3 % increase in system operational costs. Overall, through coordinating the operation of numerous residential thermal energy storage to perform both energy arbitrage and distribution network congestion management, the proposed control approach can benefit the system operation at both the national and local levels.