Electrical Energy Price Research Articles

Comparative Energy and Economic Analysis of Dish Stirling Engine and National Grid Electricity for Residential Building in Mafraq, Jordan

The primary purpose of this research is to determine the most economical approach to installing a solar dish Stirling engine (SDSE) system on a building for residential purposes in Mafraq while taking into account the local weather, usual monthly consumption of energy and the prices charged by the local powered utility. The house uses an average of 622.25 kWh of energy every month, with the highest consumption in February and the lowest in May. A range of optical efficiencies between 50% and 98% are used to mount the SDSE system. This study evaluated the relationship between the price of electrical energy and the amount of power consumed to identify the times of day when energy consumption is highest. Another approach relevant to consider is solar power, which likewise varies across the whole year. When the available intensity of the sun and power rates are at their peak, an SDSE system is regarded as a feasible solution for fulfilling the energy requirements. This is because SDSE systems can still make electricity even during cloudy days. This work also includes a comprehensive analysis of the solar power that an SDSE receives and the generated electrical power.

On-site hydrogen refuelling station techno-economic model for a fleet of fuel cell buses

Fuel cell electric buses (FCBs) have proven to be a technically viable solution for transportation, owing to various advantages, such as reliability, simplicity, better energy efficiency, and quietness of operation. However, large-scale adoption of FCBs is hindered by the lack of extensive and structured infrastructure and the high cost of clean hydrogen. Many studies agree that one of the significant contributors to the lack of competitiveness of green hydrogen is the cost of electricity for its production, followed by transportation costs. On the one hand, to reduce the investment cost of the electrolyzer, high operating hours should be achieved; on the other, as the number of operating hours decreases, the impact of the electricity costs declines. This paper presents an innovative algorithm for a scalable hydrogen refuelling station (HRS) capable of successfully matching and identifying the most cost-efficient levelized cost of hydrogen (LCOH) produced via electrolysis and connected to the grid, based on the HRS components' cost curves and the hourly average electricity price profile. The objective is to identify the least-cost range of LCOH by considering both the electric energy and the investment costs associated with a hydrogen demand given by different FCB sizes and electrolyzer rated powers. In addition, sensitivity analyses have been conducted to quantify the technology cost margins, and a cost comparison between the refuelling of an FCB fleet and the recharging infrastructure required for an equivalent fleet of Battery Electric Buse (BEB) has been performed. An LCOH of around 10.5 €/kg varying from 12 €/kg (2 FCB) to 10.2 €/kg (30 FCB) has been found for the best-optimized configurations. The final major conclusion of this paper is that FCB technology is currently not economically competitive. Still, a cost contraction of the electric energy price and the electrolyzer capital investment would lead to a 50% decrease in the LCOH. Furthermore, increasing renewable energies into the grid may shift the electricity cost curve, resulting in higher prices when the BEB recharging demand is more significant. This impact, in addition to the peak power load and longer recharging times, might contribute to bridging the gap with FCBs.

Economic Pricing in Peer-to-Peer Electrical Trading for a Sustainable Electricity Supply Chain Industry in Thailand

The state-owned power Electricity Generating Authority of Thailand (EGAT), a monopoly market in charge of producing, distributing, and wholesaling power, is the focal point of Thailand’s electricity market. Although the government has encouraged people to install on-grid solar panels to sell electricity as producers and retail consumers, the price mechanism, i.e., purchasing price and selling prices, is still unilaterally determined by the government. Therefore, we are interested in studying the case where blockchain can be used as a free trading platform. Without involving buying or selling from the government, this research presents a model of fully traded price mechanisms. Based on the study results of the double auction system, data on buying and selling prices of electrical energy in Thailand were used as the initial data for the electricity peer-to-peer free-trading model. Then, information was obtained to analyze the trading price trends by using the law of demand and supply in addition to the principle of the bipartite graph. The price trend results agree well with those of price equilibrium equations. Therefore, we firmly believe that the model we offer can be traded in a closed system of free-trade platforms. In addition, the players in the system can help to determine the price trend that will occur according to various parameters and will cause true fairness in the sustainable electricity supply chain industry in Thailand.

Determining cost-efficient controls of electrical energy storages using dynamic programming

Volatile electrical energy prices are a challenge and an opportunity for small and medium-sized companies in energy-intensive industries. By using electrical energy storage and/or an adaptation of production processes, companies can significantly profit from time-depending energy prices and reduce their energy costs.We consider a time-discrete optimal control problem to reach a desired final state of the energy storage at a certain time step. Thereby, the energy input is discrete since only multiples of 100 kWh can be purchased at the EPEX SPOT market. We use available price estimates to minimize the total energy cost by a rounding based dynamic programming approach. With our model non-linear energy loss functions of the storage can be considered and we obtain a significant speed-up compared to the integer (linear) programming formulation.

A Risk-Based Planning Approach for Sustainable Distribution Systems Considering EV Charging Stations and Carbon Taxes

Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewable production, and energy prices. Therefore, this work proposes a novel model for the multi-period planning of EDSs and DERs considering conditional value at risk (CVaR) to manage fluctuations in generation cost and carbon emissions. The proposed mathematical model aims to minimize the net present cost related to investment, operation, and risk. Unlike previous approaches, uncertain behavior of demand growth per planning period is addressed, and the risk is evaluated from two perspectives: planning costs and carbon taxes. Investments in substations, lines, renewable distributed generation, EV charging stations, and energy storage systems are considered. The uncertainties associated with the variability of renewable generation and demand are modeled through a set of scenarios. Finally, the model was evaluated using the 24 and 54-bus EDS. Thus, the proposal is a flexible tool that can be used for different purposes (e.g., carbon taxes, budget limits).

Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

AbstractNowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners’ behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub.

Assessment of the Formation of the Economic Effect of Cross-Subsidization in the Electric Power Industry

The materials presented in the article reflect changes in the mechanism of cross-subsidization in the electric power industry, where price discrimination of consumers has taken place until recently. Historically, the established practice has been expressed in the redistribution of the burden of paying for electric energy between consumer groups, ensuring the containment of tariff growth. However, the reduction of tariffs for some categories of citizens was achieved by increasing them for others, social justice was violated and, for some buyers, the price is set above marginal costs, and for others—lower, although, in general, it led to prices corresponding to average costs. This policy was considered by the state as a measure of social support for the population but, at the same time, led to some negative consequences. The presented conceptual provisions for the creation of unified energy zones aimed at ensuring a fair redistribution of cross-subsidization between electricity consumers and a gradual reduction in its consumption will make it possible to form a unified energy space of Russia. On a concrete example, the socio-economic effects of the introduction of the author’s approach to the distribution of cross-subsidization between groups of consumers of electric power transmission services are identified and shown. They show the degree of influence of the amount of cross-subsidization paid by the consumer company on the cost of the final products that this company produces. The price of electric energy, which is part of the cost of production, significantly changes the consumer cost of energy-intensive production. The implementation of the proposals contained in the article will create economic incentives for the development of economically lagging regions and will make it possible to form an effective state tariff policy in the process of regulating the socio-economic development of the Russian Federation, defining an effective state tariff policy.

Enhancing smart grids with a new IOT and cloud-based smart meter to predict the energy consumption with time series

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features.

A Joint Clearing Model of Energy-Frequency Modulation Based on Flexible Block Order

The large-scale integration of renewable energy into the grid has led to a gradual diversification of power generation trading units on the power-side, with varying operational characteristics, costs, and trading needs among diverse power generation trading units. Traditional power system clearing models face challenges. Block order is a bidding type that allows for multiple volume–price combinations. At the same time, the randomness and volatility of renewable energy poses challenges to the safe and stable operation of the system. Building a power system clearing model that meets the diverse and flexible needs of the power system has become an important consideration. Therefore, this paper considers the design of three flexible energy blocks: a sustaining block, flux block, and adjustment block to meet the differential needs of diverse trading units and establishes a flexible block order clearing model. Secondly, it establishes a joint clearing model of electrical energy and frequency modulation (FM) to ensure the stable and reliable operation of the system, and solves the model based on relevant constraints to determine the winning electrical energy price and FM price. The CPLEX and Yalmip algorithms are used to solve the model. Finally, a case study was conducted based on an improved IEEE 14-bus system. The results showed that the model proposed in this paper can satisfy the differential needs of diverse trading units, effectively improve the renewable energy consumption capacity, and reduce the prices of the energy market and frequency modulation market. Also, the standard deviation of the net load of the system is relatively low, which improves the reliability of the power system’s operation.

Reinforcement Learning in Power System Control and Optimization

Abstract Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the explanations of the concepts and principles of RL, in the paper are presented practical RL models for control and optimizing operation of power system – controlling tap-changers for maintain voltage levels and model for techno-economical optimizing operation of energy storages of households in microgrid. Trained RL agent in the practical example synchronizes operation of tap-changers to maintain satisfactory voltage level for the consumers, even in the network with distributed generation. Energy storages are in wide use in households, especially in the combination with PV. In the second example, microgrid’s energy management system (EMS) RL agent after learning process act in the simulated environment with variable electrical energy prices, variable load profiles and efficiency of PV modules of households to maximize profit for the houseowners in the microgrid. Agent controls charging and discharging of energy storages and obtain maximal benefit in randomly determined conditions of microgrid operation and different tariff situations. Models are implemented in the Python programming environment Python with specialized power system simulation software (Pandapower) and RL libraries (RLib, OpenAI).

Kajian Lingkungan dari Potensi Pembangkit Listrik Tenaga Surya di Energy Park Universitas Proklamasi 45

<p><em>Indonesia is a country that is located right on the equator. This is certainly very supportive of one of the accelerations of Solar Power Plants. Proklamasi 45 University as a university in Yogyakarta that focuses on renewable energy plans to build an energy-themed location within the campus environment, named “Energy Park” with grid-connected Solar Power Plants as a source of electrical energy. This research aims to calculate the potential for electrical energy, its contribution to reducing CO2 emissions, and the feasibility of the Energy Park project from an economic point of view. The method used in this research is comparing data in the field and the system simulation method with PVsyst software as well as cash-flow analysis calculations. According to the results of the system simulation, the potential for electrical energy generated by rooftop grid-connected PV mini-grid each year is calculated at 8,310 kWh or 8.31 MWh, with details per kWp of 1528 kWh/kWp/year. Then the contribution of reducing CO2 emissions from the implementation of Energy Park’s Solar Power Plants with electricity production of 8.31 MWh/year for 30 years is 148,2 tCO2. Through cash-flow analyst calculations, it is known that the Energy Park’s Solar Power Plants project is not economically feasible when referring to the current selling price of electrical energy and the performance of the existing Energy Park’s Solar Power Plants.</em><em></em></p><p><em><br /></em><em></em></p>

Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method

The utilization of energy hubs (EHs) in the power system and simultaneous energy management of several energy networks is of high importance to increase energy efficiency. Therefore, this paper focuses on the energy management of grid-connected flexible energy hubs. Scheme includes electrical and heating networks, where renewables and storage devices are in connection using the hub as a coordinator. Problem minimizes the expected operating cost of the mentioned networks subject to networks optimal power flow model and the mathematical representation of flexible EHs. Uncertainties related to the amount of demand, price of electrical and heating energy, and renewable power have also been considered in the problem formulation. The unscented transformation method has been adopted to reduce the volume of the problem to obtain the optimal solution in low computation time, along with the precise modeling of flexibility and modeling of the aforementioned uncertainties. Modeling the flexibility of EHs in the presence of renewable sources, modeling bio-waste energy unit for the simultaneous generation of electric and heat energy, and modeling the various uncertainties of load using the unscented transformation are among the contributions of the paper. The obtained numerical results demonstrate the capability of the proposed scheme in improving the economic and operating status of electrical and heating networks, along with improving the flexibility of EHs in such a way that energy storages in renewable hubs can extract 100% flexibility conditions. In these conditions, the hubs are able to reduce energy costs, energy losses, voltage drop, and temperature by about 25%, 24%, 60%, and 59%, respectively, compared with the power flow studies of the networks under study. Also, based on sensitivity analysis results, the increase in load and energy price leads to an increase in the operating cost of the networks, while the increase in the size of renewable resources and storage devices reduces the networks’ energy cost.

Energy profit evaluation of a photovoltaic system from a selected building in Jordan

The purpose of this study is to find the most profitable way to construct a photovoltaic (PV) system on a residential building in Amman by taking into account the local climate, the average monthly energy use, and the rates charged by the local electric utility. The building has a monthly energy usage of 550 kWh on average, with the biggest demand for energy occurring in the months of January and December and the lowest demand occurring in the months of April, May, and June. The photovoltaic (PV) system is mounted at a variety of angles, including 10°, 20°, 32°, and 54°. This study took into account the fact that the price of electrical energy is dependent on the demand for electricity in order to determine the periods of the day when there is the greatest need for energy. Another crucial aspect to consider is the sun's strength, which shifts during the course of the year. When solar intensity is at its peak and power rates are at their highest, a PV system is considered as a realistic choice to satisfy energy needs. This is because PV systems can generate energy even in cloudy conditions. A comprehensive examination of the solar energy that is taken in by a PV cell and the electric energy that is produced as a result of this is also included in the study.

Process as a battery: Electricity price aware optimal operation of zeolite crystallization in a continuous oscillatory baffled reactor

The electrification of chemical processes links the production costs and the CO2-footprint of the process industry more and more tightly to the price of electric power and the availability of power from renewable sources. Both for economic and for ecologic reasons, it is desirable to adapt the consumption to the availability of power from renewables. This work considers the optimal dynamic operation of a zeolite crystallization plant in a continuous oscillatory baffled reactor. The demand-side management problem is first investigated using a steady-state model. For the dynamic optimization problem, a moving horizon approach is introduced, and the effect of the terminal sell-off on the dynamic optimization is minimized by the usage of a terminal cost and additional horizons. The performance of the scheme is tested using different scenarios for the electric energy prices. Additionally, the influence of the uncertainty of the predictions of the price of electric power is addressed.

Electrified and non-electrified railway infrastructure - economic efficiency of rail vehicle change

The economic efficiency of the provision of services in railway transport is higher if the transport is carried out on electrified lines. However, carriers often carry out transport on routes where they use both electrified and non-electrified railway lines. This paper deals with the economic efficiency of railway vehicle change if change the electrification (electrified to non-electrified or vice versa) from undertakings providing railway freight transport services. We suggested the methodology to decision-make process of RV change if the change electrification of railway line in the same transport relation from economic efficiency point of view. We researched how the changing of input data and conditions influence the break-event point. In this paper we modelled the length of electrified rail line and price of electric energy. We have reached to a significant result, despite the high prices of electric energy, in many cases it is more economic efficiency to realize rail vehicle change when changing traction than to use a diesel RV for the whole of transport relation.

Competitiveness of Slovak power plants in connection with global changes in the development of electric energy prices

A cost framework we applied to provide an overview of bioenergy issues related to the process with renewable fuels and focuses on several possible profitability scenarios and the future development of Slovak power plants in a competitive electricity market. The aim of the paper is to propose alternative scenarios and forecast the development of Slovak power plants. The economic benefits of transitioning to alternative biofuel were analysed within the research in cooperation with the Vojany Black Coal Power Plant (EVO). To confirm the most economically efficient types of fuels used in the combustion process was used Data Envelopment analysis (DEA). Within the methodology used were also identified the factors influencing the price of electric energy (EE) on the market and subsequently the profitability of power plants. Scenarios for the future development of EE prices were identified using the statistical method FORECAST.FUNCTION. Despite the predicted increase in the market price of EE we conclude that the use of co-combustion fuels is socially beneficial compared to the use of biofuels under a wide range of assumptions regarding technical, economic and environmental characteristics. The relationship and extent of influence between coal, emission allowance and oil prices, determining EE prices were quantified by regression analysis and Analysis of Variances (ANOVA). The paper contains current data from available databases and market forecasts and the absence of analyses for economic development, sustainability, and competitiveness of Slovak power plants in the unfavourable period of the ongoing COVID-19 pandemic and war conflict Russia-Ukraine.

A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

Impact of Charging Stations for Electric Vehicles on the Power Distribution Network

Abstract The paper describes the impact of the load increase caused by the connection of electric vehicle (EV) charging stations in the power distribution network. The power distribution network model was created using the professional software tool DIgSILENT PowerFactory (DPF). Analyses were carried out for the cases of connecting a different number of EV charging stations to 0.4 kV busbars of distribution transformer stations (TS). The results of voltage conditions and loading of the most loaded distribution substations are shown. Increasing the load results in an increase in losses in the network, which is particularly significant today since the prices of electrical energy on the market are currently at historically high levels. Distribution System Operator (DSO) should pay attention to the problem of losses increase in the network, and in addition to classic solutions in strengthening the network, the DSO should also consider modern solutions such as the flexibility of new consumers.

THE APPLICATION OF DERIVATIVES BY ENERGY COMPANIES IN PRICE RISK MANAGEMENT

The process of deregulation on the energy markets has changed priorities and introduced new responsibilities into risk management. Awareness has increased among market participants regarding the importance and necessity for the risk management of electrical energy prices. Due to the nature of electrical energy, there are significant differences in price risk management in comparison to the classical methods used on financial markets. Energy derivatives have an important role in providing price signals, determining correct energy prices and facilitating effective price risk management. The article presents the results of an investigation conducted on a sample of energy companies comprised of members of the European Federation of Energy Traders (EFET). The investigation demonstrated that 75 % of the enterprises from the sample manage the risk from the variable prices of electrical energy by using some type of derivative. The most frequently used instruments are as follows: forwards (68,2 %), options (52,3 %), futures (50 %) and swaps (43,2 %), known as plain vanilla derivatives. Furthermore, it has been demonstrated that the size of enterprises is an influential factor in deciding whether to use derivatives.

ЗАГАЛЬНІ ВИМОГИ ДО ПОБУДОВИ ТА ВИКОРИСТАННЯ ГІБРИДНИХ МІКРОСИСТЕМ В РОЗПОДІЛЬНИХ МЕРЕЖАХ

The article substantiates the expediency of forming hybrid microgrids that can be integrated into a centralized power supply system or operate autonomously in order to expand the use of renewable energy sources and prevent their possible negative impact on electrical network modes. A two-level approach to managing the operation of a hybrid microgrid is proposed, which involves a preliminary planning stage based on forecasted loads, output power of renewable generation sources, market price of electric energy, and a stage of operational control based on data obtained in real time. A number of objective functions and constraints are proposed to solve the task of optimal planning of the operation of a hybrid microgrid as a multicriteria problem. At the same time, the possibility of transforming this problem into a monocriteria one is substantiated. An analysis of the advantages and disadvantages of each of the proposed approaches was carried out. Ref. 12.