Fluctuating Electricity Prices Research Articles

Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE)

There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month.

Model Prediction XGBoost for Perspective of Closing Cernavoda Reactor 1 and the Impact on the Romanian Energy System

Abstract Employing artificial intelligence (AI) and machine learning (ML) technologies to analyze the progression of energy markets is imperative, as these methodologies facilitate the modeling of diverse domain-specific data to forecast developments in an exceedingly volatile market. Utilizing machine learning algorithms to model data concerning the Romanian energy sector proves beneficial, particularly in circumstances where significant changes necessitate adaptation to internal consumption demands, while mitigating substantial fluctuations in electricity prices. Such fluctuations can profoundly impact crucial national economic indicators.. In this paper, we aim to simulate such a situation for the moment when Romania will be obliged to shut down and modernize Reactor 1 at Cernavodă due to reaching the standard operating time for the CANDU model. We will use an XGBoost for Regression algorithm in which we will input data about the national energy system (consumption, energy quantities according to the energy system's production structure by resource types) from the period 2020-2023, and then we will simulate the shutdown of the reactor to analyze how the national energy market is influenced. Our findings not only reveal the indispensable role of predictive analysis through ML/AI models in anticipating market changes, but also provide practical insights for decision-makers aiming to optimize the national energy system so that when Reactor 1 at Cernavodă is shut down, the domestic market is affected as little as possible, considering that this reactor has supplied the national system with approximately 700 MWe/h over the past 20 years.

Operation optimization of cogeneration microgrid based on deep reinforcement learning

Abstract The deep Q learning method is adopted to transform the microgrid model into the Markov decision model to realize the coordinated management and energy optimization of the microgrid. For the problem of the Markov decision model, we cleverly divide the working state of the gas turbine into different gears to meet its requirements. To reduce the fitting difficulty and improve the result accuracy and convergence stability, a linear penalty term was added to the reward function. Considering the operating and environmental costs, in addition to the purchase and sale price of a large power grid, peak and valley electricity price and operation constraints, we further reduce the peak and valley difference and reduce the operating cost. Finally, the output of the battery and the output of the distributed power supply are obtained through simulation verification. Through the peak-shifting and valley-filling function of the battery, it can effectively deal with the fluctuation of peak and valley electricity prices, reducing the peak and valley difference of electric heating load, and providing strong support for sustainable energy management.

Floating solar power plant planning based on electricity price fluctuations in Towuti, south Sulawesi

A company located in South Sulawesi, intends to mitigate emissions from the Power Plant by constructing an environmentally friendly alternative, namely the Floating Solar Power Plant (FSPP), in Towuti. The development plan involves a feasibility analysis for each configuration of the FSPP, considering technological, economic, and environmental criteria. The selection of the preferred configuration is determined using the Analytical Hierarchy Process (AHP) method, based on input from experts. According to this method, the chosen configuration is the on-grid non-battery Floating Solar Power Plant, with a Net Present Value of Rp. 1,431,159,245,752, an Internal Rate of Return of 9.46%, a Payback Period of 8.916 years, a Profitability Ind ex of 1.299, a system efficiency of 21.56%, a carbon footprint of 35,003 kg CO2, and an emission reduction of 9,136,808,273 kg CO2. Decision analysis, involving open and abandonment scenarios based on fluctuations in industrial electricity prices, was conducted using Monte Carlo simulation, yielding a volatility value of 31.2%. The real options analysis recommends abandonment in the twentieth year, with a decision success potential of 38%, based on the results.

Innovative decision-making for solar photovoltaic system installation in residential energy hubs: A probabilistic dynamic planning approach

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions.

Energieflexibilisierung von Kälteversorgungssystemen

Abstract In order to reduce the energy-related operating costs of industrial cooling supply systems, demand side management becomes more and more economically attractive in the face of increasing electricity price fluctuations. However, the realization of this potential usually fails due to implementation. In order to reduce the impediments to implementation, this paper presents a rule-based model predictive approach to operational optimization based on thermodynamic relationships and logical decision criteria. The optimization approach is finally validated on a simulation model as well as on a real climate room and leads to realized operating cost savings of up to 21 %.

Multivariate low-carbon scheduling of distribution network based on improved dynamic carbon emission factor

The “load-following” characteristic of the power system makes the electricity consumption behavior on the load side crucial for the low-carbon operation of the distribution network. To address this, this paper proposes an improved dynamic carbon emission factor for the distribution network, taking into account the spatiotemporal characteristics of carbon emission intensity and the integration capacity of photovoltaics (PV). Based on this, a calculation method for the carbon emissions of the distribution network load is provided. Subsequently, for commercial and industrial user scenarios, demand response models are separately constructed for commercial and industrial loads based on different driving mechanisms. Using time-of-use electricity prices as decision variables, optimization scheduling of the distribution network is carried out with the objectives of minimizing scheduling costs and carbon emissions. At the same time, a case study is conducted in an improved IEEE-33 node distribution network. The results indicate that, under the guidance of the improved dynamic carbon emission factor, load transfer can be achieved through fluctuating electricity prices, effectively reducing the scheduling costs of the distribution network, decreasing carbon emissions, and enhancing the PV integration capacity of the distribution network in different user scenarios. Finally, it is hoped that in the future, this optimization method can be widely applied, and further research can explore coordinated strategies among generation, network, load, and storage to advance the development of the power industry.

Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets

Electricity market liberalization and the absence of cost-efficient energy storage technologies have led to the transformation of state-owned electricity companies into complex electricity market entities, each having a different time horizon. Deregulation has intensified competition, giving rise to increased uncertainty caused by a multitude of interrelated exogenous factors, resulting in unexpected fluctuations in electricity prices. As a consequence, market participants encounter elevated risks and seek effective mitigation strategies. In this paper, the challenges described in the literature are addressed by studying price distribution histograms in the German and Finnish electricity markets. The objective is to identify normal price intervals that can serve as a foundation for an integrated Day-Ahead price forecasting methodology. A novel approach utilizing the Extreme Learning Machine in combination with Bootstrap intervals is proposed and applied to both markets. The findings demonstrate that Bootstrap intervals effectively capture normal prices, whereas extremely high prices typically align with the upper limits of Bootstrap intervals. Conversely, negative prices tend to fall outside the lower boundaries of the intervals. In order to assess the performance of the proposed methodology, a comparative analysis of its forecasting accuracy against the well-established Generalized AutoRegressive Conditional Heteroskedasticity and AutoRegressive Fractionally Integrated Moving Average models is conducted. In addition, both the computational efficiency and forecasting accuracy of the Extreme Learning Machine in comparison to the Artificial Neural Network are assessed. The results reveal the superior efficiency of the Extreme Learning Machine. The developed forecasting model could potentially assist market participants in making well-informed decisions and executing optimal bidding strategies in response to various scenarios before the Day-Ahead market closes. Notably, the proposed methodology transcends the limitations of fixed price thresholds and effectively addresses market nuances, including the occurrence of negative prices, thus offering a more comprehensive approach for electricity price forecasting.

Energy optimization management of microgrid using improved soft actor-critic algorithm

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

Sustainable Energy Management using Machine Learning

The global energy sector is encountering escalating difficulties, including rising demands for efficiency, shifts in supply and demand patterns, and a lack of optimal management analysis. Utilizing machine learning (ML) to process energy sector data can gradually address these issues. ML algorithms have the capability to analyze equipment data, construct predictive models, and address sustainability-related problems. In smart cities, the integration of machine learning algorithms enables automatic responses to fluctuations in electricity prices, facilitating effective control of energy consumption. Systems employing machine learning can assist energy suppliers in adapting to variable renewable energy supplies. Worldwide, there is a growing emphasis on low-emission energy sources, leading to increased installed capacities of solar photovoltaic, wind farms, and marine energy systems. Consequently, artificial intelligence and machine learning are poised to play a vital role in effectively managing the challenges of the energy sector. The implementation of micro-grids presents significant challenges that necessitate advanced control techniques like model predictive control (MPC). This paper focuses on employing MPC for energy management in micro-grids and aims to provide an up-to-date overview of the development of MPC methods for sustainable energy management.

Performance analysis of the comprehensive energy system based on active energy storage-discharge technology under time-sharing electricity price operation strategy

Multi-energy complementary comprehensive energy systems based on renewable energy can reliably meet the energy needs of communities and significantly mitigate carbon emissions. This is of paramount significance in the establishment of low-carbon sustainable communities. The effective operation of an integrated energy system necessitates a thorough examination of demand-side load, the dynamic performance of the energy supply system, the configuration of energy storage systems, local electricity pricing policies, and operational or control strategies for the system. It confronts substantial challenges in practical application. Considering the noteworthy performance variations of comprehensive energy systems under diverse demand-side load, fluctuating electricity prices, and various operating modes of energy storage equipment, this paper initially conducts a practical analysis of demand-side load. A comprehensive energy system with multi-energy complementary based on source-load-storage coordination (SLS-CES) model was constructed. From the perspective of system operation strategy, we identify the current electricity consumption pattern in the region, characterized by peak and valley periods. We integrate this information with the time-of-use electricity pricing policy and the equipment within the target area. An operation strategy based on local time-of-use electricity price policy was proposed. An in-depth analysis of the thermal performance, environmental performance, and economic performance of the system in the full-year dimension and typical daily dimension under this operating strategy was conducted. The results show that under this operation strategy, the system's annual net income is 415.90 thousand yuan, of which the system's energy storage battery power supply revenue during peak period reaches 254.40 thousand yuan. The initial investment is 5.03 million yuan, and the static investment payback period is 12.09 years. The annual primary energy saving rate of the system is 36.00%, and the maximum CO2 emission reduction rate is 72.23%. The maximum primary energy saving rates on typical days in winter and typical days in summer are 74.74% and 97.14% respectively. The total annual CO2 emissions were reduced by 5.47 tons and 4.54 tons respectively. Through this study, we found that a system incorporating energy storage equipment, combined with an operation strategy based on electricity price policy, can yield additional economic benefits. This approach helps alleviate peak power supply pressure on the grid and mitigates the challenges associated with the high initial investment in renewable energy systems, all while maintaining good thermal performance and environmental benefits.

Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.

A novel probabilistic forecasting system based on quantile combination in electricity price

In the electricity market, the accuracy of electricity price forecasting is significant for real-time control; however, the complexity and volatility of electricity prices make this a challenge. Existing forecasting models focus on deterministic forecasting and rarely address the uncertainty in electricity price forecasting. Therefore, this study fills this knowledge gap by introducing a novel combined probability forecasting system (CPFS) and creatively incorporating probability density estimation based on kernel functions in a multi-objective optimization algorithm. In addition, to effectively integrate the forecasting components, the tuna optimization algorithm was enhanced to overcome the limitations of traditional multi-objective optimization algorithms. Finally, the validity of the CPFS is confirmed through two electricity price cases, considering three equally important aspects: reliability, resolution, and sharpness. From a comprehensive perspective, CPFS outperformed the most advanced benchmark by more than 5.66% and 38.93% in AIS and by more than 13.41% and 3.55% in quantile loss on the NSW and Singapore datasets, respectively. The experimental results demonstrate that the CPFS provides an effective range for electricity price fluctuations. Furthermore, given that probabilistic forecasting is essential for risk management, it offers important implications for the electricity market.

Multi-agent-based real-time operation of microgrids employing plug-in electric vehicles and building prosumers

Real-time operation of microgrids holds significant importance due to its ability to enhance energy resilience and efficiency. Real-time control in conjunction with continuous monitoring and energy supply and demand fluctuations adaptation, ensures a stable and reliable power supply for local communities, even during grid disturbances or outages. Moreover, real-time operation supports demand response mechanisms, enabling users to actively manage their energy consumption and costs. Overall, it plays a pivotal role in modernizing and future-proofing contemporary energy infrastructure, making it more sustainable. In this paper, an efficient, easily applied multi-agent-based real-time energy management and control system for microgrids employing Plug-In Electric Vehicles (PEVs) and building prosumers is developed. The primary goal of this proposed approach is to determine the optimal power set-points for all microgrid components with the objective to reduce the overall daily operation expenses under fluctuating electricity price, while simultaneously satisfying a wide range of constraints. Well-defined flexibilities of all microgrid components to adjust their power are harnessed to execute optimal power dispatch. The implementation of the proposed method resulted in a noteworthy cost reduction of 14.3% when contrasted with a typical operation of the microgrid.

Auditory-circuit-motivated deep network with application to short-term electricity price forecasting

Reliable electricity price forecasts are of great importance to operators and participants in power markets. However, due to mixing effects of various factors, electricity price fluctuations are too complex to extract hidden features for accurately modelling. Additionally, biologically-inspired ideas are promising in significantly improving the performance and rationality of deep forecasting networks. Based on the fact that the auditory system effectively handles complex sound signals with amplitude, frequency and wavelength characteristics, this paper explores an auditory-circuit-motivated deep network with three following modules for forecasting short-term electricity prices. Specifically, the coding module imitates that left and right ears receive the sound and convert it into electric signals in parallel, and codes the input electricity prices into multiple valuable features, improving the feature integrity and model stability. The analysis module imitates that the left and right hemispheres integrate and handle electric signals to produce higher auditory information, and captures nonlinear and short-term dependencies via stacking convolutional and gating operations. The forecasting module imitates that the high-level brain region pays attention to the external environment based on higher auditory information, and generates final forecasts via the attention mechanism. Based on the cooperation of the above three modules, the proposed deep network mimics the flowchart, structures and functions, thereby inheriting superior handling capability of the auditory circuit. Experiment results under two real-world sets show that the proposed deep network is superior to 13 baselines, and improves the mean absolute error by averages of 14.7 % and 20.6 % in normal parts, 12.9 % and 23.4 % in high-fluctuation parts, and 16.1 % and 16.2 % in peak-low parts, which is an effective supplementary model for electricity price forecasting.

Assessing Techno-Economic and Environmental Impacts of Gas Compressor Fleet as a Source of Flexibility to the Power System

In the Great Britain's high-pressure gas transmission network, there are 24 gas compressor stations with 68 operational compressor units of different types and sizes. As of 2021, the aggregate capacity of electric-driven gas compressors was 200 MW. By using linepack as a form of gas storage buffer, the compressors can be operated flexibly to achieve a minimum operational cost under fluctuating electricity prices. Furthermore, since electricity consumed by electric-driven compressors can affect the balance of the power grid, the ability of electric-driven compressor units to regulate their electricity consumption is useful to be quantified. In this paper, the value of flexibility from the hybrid compressor stations (compressor stations in which both electric-driven and gas-driven compressor units exist) is analysed. The operation of the high-pressure gas network in Great Britain was formulated as a Mixed-Integer Second Order Cone Programming (MISOCP) problem. The modelling results show that employing the flexibility from hybrid compressor stations in the gas network can reduce more than 20% of the operational cost and 50% of emissions from the compressor fleet. Furthermore, a cost-benefit analysis was carried out to assess the investment viability of installing electric-driven compressor units. It is demonstrated that the economic feasibility of investing in electric-driven compressors is sensitive to the future carbon price.

Energy supply scheduling in manufacturing systems using Quantum Annealing

Optimizing a manufacturing company's in-house energy demand amidst fluctuating electricity prices and uncertainties in renewable energy supply as well as volatile manufacturing planning situations is a challenging task. To tackle this issue, a novel approach is developed for scheduling the energy supply in manufacturing systems with the objective of reducing energy costs. The approach employs Quantum Annealing to determine the optimal mix of in-house generation, purchased electricity, and energy storage. The effectiveness and scalability of the approach are demonstrated through the validation using two simplified use cases, showcasing its potential in solving complex energy supply optimization problems.

The electric shock: Causes and consequences of electricity prices in the United Kingdom

This study explores the primary causes of wholesale electricity price fluctuations in the United Kingdom (UK). Using the structural vector autoregression (SVAR) model that identifies both supply-side and demand-side shocks, and monthly data for the period from January 1996 to May 2022, the findings show that the impact of electricity price increases on the real electricity price is dependent on the underlying cause of the price increase. Electricity price movements in the most recent period of global hardships from 2020 to 2022, including the Covid-19 pandemic and the Russian invasion of Ukraine, are further examined, which were widely discussed to have affected the global energy segment. The findings show that although shocks to natural gas had a part to play in the recent price increase of electricity, some major contributing factors remain unique to the UK. Finally, the findings put into perspective and question the effectiveness of the energy cap considering the large contribution of renewable power generation.

Information gap decision theory-based optimization of joint decision making for power producers participating in carbon and electricity markets

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.