Electricity Purchase Research Articles

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

EFFICIENCY OF A WIND PLANT IN REAL CONDITIONS WITH ADDITIONAL COSTS AND THE MARKET PRICE OF ELECTRICITY

In its development, the global energy sector is constantly demonstrating the growth of electricity consumption. Traditional power plants in this process ensure an increase in greenhouse emissions and aggravation of climate problems, which are becoming more relevant every year. Against this background, modern alternative energy sources, namely wind power plants, contribute to reducing the impact of traditional power plants and encourage solving environmental problems. The development of global wind energy is accompanied by the growth of unit capacities of generators of wind power plants. The paper analyzes the efficiency of a wind power plant based on modern wind generators for predetermined location conditions, taking into account operating costs based on operational experience and based on the assessment of the power plant's participation in the balancing market of electricity with a waiver of the "green tariff". Calculations of the technical and economic indicators of an imaginary designed 4-unit wind power plant with various wind generators under the wind conditions of a predetermined industrial site located in the Carpathian region of Ukraine were carried out. The calculations take into account the modern typical characteristics of wind generators, additional operating costs and the estimated cost of electricity purchase of the station under the "green tariff" and in the conditions of a balancing electricity market to compare economic efficiency. In order to make an optimal design technical decision with a comparative assessment, three types of wind generators were determined for installation at an imaginary wind farm - Enercon E-175 EP5, Vestas V136-4.5 MW and Vestas V172-7.2 MW. In the conditions of the predetermined construction site of the projected wind farm on the Carpathian ridges of Ukraine, the measured and calculated annual probability distribution of wind speed according to the analytical Weibull distribution is applied, and the characteristics of the annual productivity of the 4-unit wind farm for the adopted three types of wind generators are given. The technical and economic characteristics were obtained in the work: capital investment, operating costs, characteristics of electricity production, the gross profit during the annual operation of the wind power plant with three options for installing wind generators was calculated, making it possible to estimate the payback time of the investment in the construction of the wind plant. So, for a 4-unit station with generators of the Enercon E-175 EP5, the payback period is 3.3 years, and with wind generators of the Vestas V136-4.5 MW - 3.4 years, as well as for the Vestas V172-7.2 MW in operating conditions of the "green tariff" and, respectively, 2.9 years, 2.9 years and 3.0 years under conditions of sale of electricity at balancing market prices. Additional operating costs for the disposal of worn wind turbine blades do not have a significant impact on the overall cost indicators and amount to 5-7% of the total costs.

Forecasting volumes of electricity sales using a multilayer neural network

Energy consumption is related to all types of human economic activity. Energy production significantly affects the state of the environment; therefore, high quality planning of electricity consumption is a priority issue in the development strategy of any country. All responsibilities for planning electricity consumption and purchasing electricity fall on energy supply companies. High-quality planning of electricity consumption will lead to a reduction in the costs of energy supply companies, which will have an impact on reducing the costs of creating final products for the average consumer. This article is devoted to the problem of increasing the efficiency of energy supply companies to meet the needs of household consumers. The work substantiates the necessity of planning electricity supplies in the necessary volume to ensure the rational use of energy and economic resources. The problem of performing forecasting by a person is described, when the result depends both on the analyst's qualification in the subject field and on his qualification in analysis methods. Arguments are presented regarding the use of modern methods of artificial intelligence - neural networks, which have the ability to learn on a set of data and allow to reveal hidden relationships and regularities between data. The article provides an analysis of the latest research and publications on the use of artificial neural networks in energy forecasting, which confirmed the feasibility of using the chosen method for a given problem. The process of using a multilayer neural network for forecasting the volume of electricity sales to consumers is described. The nature of the data describing the volume of monthly hourly electricity purchases for household consumers is given, an example of a time series is shown. The peculiarities of the input parameters for the neural network are determined: month of the year, day of the week, type of day (holiday or working), the average maximum value of hourly electricity purchase

Peculiarities of managing the economic behavior of enterprises producing oil crops

The development of oilseed production in Ukraine is a strategically important area, as it creates competitive advantages in meeting demand on foreign and domestic markets. The purpose of the study is to determine the peculiarities of managing the economic behavior of enterprises producing oil crops in Ukraine and its manifestation in modern conditions. According to the results of the study, it was proved that the model of economic behavior of an enterprise specializing in the production of oil crops reflects the degree of its participation in economic processes at various levels, financial and organizational capabilities, as well as determines the position in the market of relevant crops and in solving social problems of modern life. It has been proven that the economic behavior of the company will vary depending on the state of the economy in the country where the business is based. It was also found that positive changes in the external environment can cause corresponding changes in the internal environment and in the economic behavior of enterprises. They will be able to reorient their activities in favor of domestic consumers. In fact, it should be considered when forming the state policy for the development and support of the agro-industrial sector. Furthermore, these transformations will be facilitated by the restoration of normal, safe operation of enterprises, a transparent procedure for the purchase of electricity, and the growth of demand for oilseed products in the domestic market. The problems accumulated during the martial law, which were superimposed on previously unresolved crisis phenomena in the agricultural sphere, the chaotic economic behavior of many farmers today indicate that without a thorough understanding of the motives of the enterprises' behavior, the choice of ways of effective economic development becomes difficult and uncertain. Modern transformational processes require changes in the management system of enterprises through the formation of new directions of economic behavior. Transformations during the recovery of enterprises and orientation towards their development in the post-war period are already forming new rules and norms of behavior regarding interaction with other market subjects (consumers, suppliers, intermediaries, financial institutions). Keywords: management of economic behavior, functioning of the enterprise, efficiency, production of oil crops, state of the country's economy.

Artificial Intelligence Based Smart Home Energy Management System: A Review

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

Application of blockchain technology in autonomous electricity transaction and settlement at the end of distribution network

Abstract With the deepening of the concept of low-carbon environmental protection, the use of renewable energy in social production has become more and more popular. More and more consumers in the distribution network have begun to change their identities to become consumers with the continuous reform of the electricity sales side. Electricity trading and settlement methods in the electricity market directly affect the relevant income of market entities, so a fair and reasonable settlement mechanism is an important factor for the normal operation of the electricity market. Electricity trading refers to the buying and selling of electricity commodities and services, including electricity trading, auxiliary service trading, and transmission rights trading. With the expansion of the electricity market, how to deal with the deviation of electricity is also a difficult problem that requires electricity settlement. This paper aimed to study the application of blockchain technology in the autonomous electricity transaction and settlement at the end of the distribution network. It was expected to use the blockchain technology to set up related transaction mechanisms, provide new solutions for electricity transaction and settlement, and improve the efficiency and fairness of electricity settlement. Blockchain technology does not rely on additional third-party management agencies or hardware facilities, and there is no central control. Except for the self-contained blockchain itself, through distributed accounting and storage, each node realizes information self-verification, transmission and management. On the basis of sorting out the principles of electricity balance and electricity price formation, this paper proposed a contract decomposition method with a higher degree of fit, and revealed the settlement principle of deviation electricity in the electricity market environment. Aiming at the application scenario of blockchain in the energy Internet, an energy trading platform based on blockchain was designed, and the test results showed the feasibility of blockchain in electricity settlement scenarios. The experimental results of this paper showed that in the traditional electricity purchase fee settlement, the average transaction price is stable at 0.6 yuan/KWh. In the blockchain settlement process, the electricity purchase is 25 yuan when the electricity purchase is 50 KWh. Therefore, blockchain settlement is used more frequently in the market, and the profits are higher.

A constrained DRL-based bi-level coordinated method for large-scale EVs charging

With the vigorous development of battery electric vehicles (BEVs), BEVs’ charging scheduling is essential for better economy and safety. In this paper, we aim to minimize the electricity purchasing cost considering a large number of BEVs and distributed energy. This problem is challenging to get the optimal charging policy due to a large number of uncertainties and dimension disasters caused by a large scale of BEVs and renewable energy. To meet these challenges, we propose an improved bi-level schedule framework, which decomposes the primal problem into two sub-problems to reduce the computational complexity and designs a communication mechanism to ensure the consistency of optimality between different levels. Then the problem is modeled as constrained multi-level Markov decision processes (CMMDP). In the upper level, a constrained deep reinforcement learning method (CDRL) is proposed to get the total charging or discharging energy of BEV groups. An action constraint module is constructed to ensure the feasibility of chosen actions and a novel reward shaping function is designed to optimize action selection. In the lower level, an optimal descending order charging policy (DOCP) is taken to fast decide the charging or discharging behavior for each BEV based on the upper level’s decision. Numerical experiments show that our method has obvious superiority in training efficiency and solution accuracy compared with state of art DRL methods, and reduces the cost by 12% to 28% compared with an experience charging policy.

Energy Recovery from Natural Gas Pressure Reduction Stations with the Use of Turboexpanders: Static and Dynamic Simulations

The application of expansion turbines at natural gas pressure reduction stations (PRS) is considered in order to recover energy contained in the natural gas. This energy is irretrievably lost at the reduction stations which use the traditional pressure reducer. Expanders allow for the electricity production for PRS own needs and for resale. The paper presents an analysis of the possibility of using turboexpanders at PRS in Poland. Authors performed static simulations for the assumed data sets and dynamic simulations for annual data from selected representative natural gas reduction and measurement stations. Energy balances are presented for the discussed scenarios that compare the energy requirements of natural gas pressure reduction stations which use a classic pressure reducer or turboexpander (TE). Using static simulations, authors investigated whether the use of a turboexpander is economically justified for the case if it is used only to supply the reduction station with electricity. Dynamic analyses were carried out using real data. In addition, static analyses were performed for a natural gas reduction and measurement station using a PEM fuel cell for the production of electricity in a combined gas heating system. At higher inlet temperatures and pressures, the expansion process was more economical due to the lower heat power requirement and the greater amount of produced electricity. The PRS with the turboexpander compared to the PRS with the reducer required the supply of thermal energy which did not allow the PRS to lower operating costs for the assumed prices of heat and electricity. The reduction system with the PEM fuel cell in the combined heating system positively achieved lower operating costs of the PRS (without taking into account the investment costs). Total annual costs for PRS with a reducer was PLN 1,593,167.04, and for PRS with TE + PEM PLN 1,430,595.60—the difference was PLN 108,571.44 in favor of the arrangement with TE and PEM. The payback time should be investigated, although the use of such a system gives the impression of oversizing. An increase in the electricity purchase price and a decrease in the natural gas purchase price may contribute to the investment in the future.

A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets

This paper proposes a bilevel game-theoretic model for multiple strategic retailers participating in both wholesale and local electricity markets while considering customers’ switching behaviors. At the upper level, each retailer maximizes its own profit by making optimal pricing decisions in the retail market and bidding decisions in the day-ahead wholesale (DAW) and local power exchange (LPE) markets. The interaction among multiple strategic retailers is formulated using the Bertrand competition model. For the lower level, there are three optimization problems. First, the welfare maximization problem is formulated for customers to model their switching behaviors among different retailers. Second, a market-clearing problem is formulated for the independent system operator (ISO) in the DAW market. Third, a novel LPE market is developed for retailers to facilitate their power balancing. In addition, the bilevel multi-leader multi-follower Stackelberg game forms an equilibrium problem with equilibrium constraints (EPEC) problem, which is solved by the diagonalization algorithm. Numerical results demonstrate the feasibility and effectiveness of the EPEC model and the importance of modeling customers’ switching behaviors. We corroborate that incentivizing customers’ switching behaviors and increasing the number of retailers facilitates retail competition, which results in reducing strategic retailers’ retail prices and profits. Moreover, the relationship between customers’ switching behaviors and welfare is reflected by a balance between the electricity purchasing cost (i.e., electricity price) and the electricity consumption level.

Towards Informed Policy Making: An Analysis of the Impact of COVID-19 on Electricity Purchases in South Africa

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa’s energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters.

Risk-averse TSO-DSOs coordinated distributed dispatching considering renewable energy and demand response uncertainties

The renewable energy generation (REG) integration has introduced a significant amount of uncertainties to power systems. This new trend requires power systems to have sufficient flexibility to mitigate the power imbalance between supply and demand. Demand response (DR), as a potential flexibility resource, has gained widespread attention in recent years. However, communication failures or customers’ unexpected behaviors may result in DR uncertainties, leading to operational risks. To cope with the impacts of DR and REG uncertainties, this work proposes a stochastic programming-based risk-averse optimal dispatch model to reduce both electricity purchasing cost and potential risk cost through transmission network (TN) and active distribution networks (ADNs) coordinations. The risk-averse distributed optimal dispatch problem is formulated as two sub optimization models. One conducts the transmission-level risk-averse generation dispatch, while the other represents the distribution-level ADN market clearings. Moreover, the conditional value at risk (CVaR) is used to measure the potential risk cost caused by DR and REG uncertainties. Finally, the optimization model is solved by a distributed solution algorithm, alternating direction method of multipliers (ADMM), to preserve data privacy between multiple stakeholders and reduce communication requirements. The performance of the proposed approach is evaluated in T30-D2 × 33 and T118-D85 + D2 × 69 test systems, the results validate its effectiveness.

A two-phase market clearing framework for inter-provincial electricity trading in Chinese power grids

To fully utilize the energy resources, inter-provincial electricity trading have been conducted in multiple Chinese provincial power grids. This paper proposes a two-phase market clearing framework to facilitate inter-provincial electricity trading in Chinese power grids. In Phase one, the pre-processing of bid curves is implemented to address the multi-segment bid behaviors of massive market participants. As the pre-processing methods, network partition, curve aggregation and curve approximation are developed to reduce the computation complexity. In Phase two, a bi-level model is established to depict the market clearing process, which includes the upper-level model for inter-provincial trading and the lower-level model for intra-provincial trading. Those two models are iteratively solved by passing the optimal electricity purchase price and electricity demands to each other. Numerical experiments based on the IEEE systems and the practical Chinese inter-provincial power grids are carried out to verify the validity and scalability of the proposed framework. The simulation results indicate that: 1) the pre-processing of bid curves effectively reduce the computational burdens; 2) the inter-provincial market promotes the utilization of energy resources and increases the total social welfare. Further, the errors caused by the pre-processing methods are analyzed and the availability of the proposed framework is discussed.

Lightweight Online Scheduling for Home Energy Management Systems Under Uncertainty

The increasing use of renewable energy sources and electrical energy storage systems creates a new energy paradigm for residential houses and buildings. Such design reduces carbon footprint, but it also introduces a new challenge for minimizing the electricity bill while still meeting users’ needs. This challenge is often accompanied by deep uncertainty in user load demands, electricity tariffs, and renewable energy generations. To address this challenge, we propose an online algorithm, Virtual Algorithm-based Lightweight Online Scheduling (VALOS), to manage electricity purchasing and battery operations. Our solution does not use any prediction components in dealing with uncertainties. Instead, our algorithm employs a lightweight routine, Virtual Algorithm (VA), for making critical decisions to manage uncertainty. We prove that VA achieves an expected probability of 1/e for choosing the optimal purchasing timing online and incurs only a logarithmic computational cost. With VA, VALOS decides how the energy storage reacts to the load demands, which incurs only a linear-logarithmic online computational cost while achieving optimal performance under the specified conditions. Finally, we conduct extensive trace-driven simulations on real-world datasets to confirm the theoretical results and demonstrate the potential of VALOS.

A sectoral view of conceptualising macroeconomics of a ‘just transition’ in India


 In light of India’s COP26 commitment of reaching net zero by 2070, it is important to understand how India could ensure a ‘just transition.’ Since the transition raises several questions regarding who will benefit from it and who will lose out, this paper offers an assessment of the sectors that will be impacted most by the transition. This includes coal, mining, power, formal manufacturing sectors, and MSMEs. Macroeconomic consequences of the transition in terms of employment intensity, energy intensity, the total value added, and export competitiveness of the above-mentioned sectors have been examined. Using data from the Annual Survey of Industries for 2017-18 and 2018-19 and key informant interviews, the paper presents a sectoral analysis of the transition in the Indian context. In terms of employment, the power and the coal sector will be affected the most. In terms of fuel use, manufacturing sectors that either use coal or purchase electricity (indirectly using coal) will also be impacted. The spatial dimension of the transition will be very important, since certain coal-producing districts will be affected the most. 

Edge intelligence enabled optimal scheduling with distributed price-responsive load for regenerative electric boilers

Heat supply accounts for a substantial amount of terminal energy usage. However, along with price rises in primary energy, there is an urgent need to reduce the average cost of energy consumption during the purchasing of thermal services. Electric heating, an electricity-fed heating production and delivery technology, has been suggested as a promising method for improving heating efficiency, due to the ease of scheduling. However, the traditional centralized operating methods of electricity purchasing rely on explicit physical modeling of every detail, and accurate future predictions, the implementation of which are rarely practical in reality. To facilitate model-free decisions in the field of electricity purchasing, heat storage, and supply management, aimed at cost saving in a real-time price environment, this study proposes a scheduling framework based on deep reinforcement learning (DRL) and the existence of responsive users. First, the structure of a distributed heating system fed by regenerative electric boilers (REBs), which facilitate shiftable heat-load control, is introduced. A terminal heat demand response model based on thermal sensation vote (TSV), characterizing the consumption flexibility of responsive users, is also proposed. Second, due to thermal system inertia, the sequential decision problem of electric heating load scheduling is transformed into a specific Markov decision process (MDP). Finally, the edge intelligence (EI) deployed on the demand side uses a twin delayed deterministic policy gradient (TD-3) algorithm to address the action space continuity of electric heating devices. The combination of a DRL strategy and the computing power of EI enables real-time optimal scheduling. Unlike the traditional method, the trained intelligent agent makes adaptive control strategies according to the currently observed state space, thus avoiding prediction uncertainty. The simulation results validate that the intelligent agent responds positively to changes in electricity prices and weather conditions, reducing electricity consumption costs while maintaining user comfort. The adaptability and generalization of the proposed approach to different conditions is also demonstrated.

An improved energy management for MVDC distribution system based on exponential droop control

The energy management optimization and DC voltage stability are the main challenges when loads surging and renewable energy sources (RESs) fluctuations occur in the medium-voltage DC distribution system (MVDC-DS). To solve these issues, this paper proposes a dual-time scale energy management method based on improved droop control for the MVDC-DS. Firstly, a ±10 kV three-terminal hybrid modular multilevel converter (MMC)-based ring MVDC-DS with DC fault ride-through capability is constructed, which including electric vehicle charging stations, energy storage systems, multiple loads and RESs. Secondly, the droop control adopted by the hybrid MMC is improved by exp-function to achieve higher power quality. On this basis, a dual-time scale energy management method is proposed to minimize the electricity purchasing cost. The reference power of each controllable unit is optimized in the long time scale, and the parameters of improved droop control are optimized in the short time scale. Finally, a case study is conducted on the ±10 kV three-terminal hybrid MMC-based ring MVDC-DS, and the results indicate that the proposed method can improve the economy and power quality of system, and help to realize the normal system operation under different conditions.

Influence of Climate Change on High School Students' Attitudes Towards Electric Vehicles

Electric vehicles are gaining interest due to their increasing affordability, improved design, and longer range. Generation Z buyers currently comprise a small percentage of new electric vehicle purchases, but will dominate as the largest consumer group in the automotive industry during the next 10-20 years. To test the level of interest in electric vehicles by the high school subset of Generation Z, 56 anonymous individuals were surveyed to solicit their opinions. The survey asked for each participant's opinion on climate change and electric vehicles in an attempt to find a correlation between a person's attitude towards climate change and that person's interest in electric vehicles. I hypothesized there would be a positive correlation between a student's view on climate change and their perception of electric vehicles. Using a Pearson's correlation study, a positive, but weak, correlation was found between the two topics. Based on the data's p value, the correlation was determined to be statistically insignificant, rejecting my original hypothesis. Through an exploratory data analysis of the survey results, it was found that most high school students are aware of climate change, but their awareness alone may not influence their lifestyle choices to lessen the problem. I found that the respondents were more concerned about the price of electric vehicles than their environmental benefits. Despite observing a weak correlation, this study indicated that if price were not a factor, purchasing an electric vehicle would be more strongly considered by those who care about climate change than those who do not.

Energy, economic, and environmental (3 E) examinations of the indirect-expansion solar heat pump water heater system: A simulation-oriented performance optimization and multi-objective decision-making

In this paper, a simulation-oriented optimization integrated with the multi-objective decision-making (MODM) model for an indirect-expansion solar-assisted heat pump (IDX-SAHP) water heater is proposed and the energy, economic, and environmental (3 E) aspects of the system are examined. The developed model is verified and its performance is scrutinized in a humid continental climate region of China. Next, the sensitivity analysis is conducted via the one-way analysis of variance (ANOVA) approach. Then, the single-objective optimization (SOO) and multi-objective optimization (MOO) are conducted via the migrating birds optimization (MBO) method to maximize the coefficient of performance (COP) and the collector efficiency (CE), singly and concomitantly. The SOO results indicate the IDX-SAHP design as per the COP-oriented optimization causes the system to operate less for about 120 hours a year in contrast to the system design as per the CE-oriented optimization. Also, the results of MOO integrated with MODM indicate that MOO gives better performance in contrast to the SOOs so that although the CE of the maximized IDX-SAHP reduces a little from 51% to 46%, its COP increases greatly from 2.5 to 7. Therefore, the overall power consumption of the system decreases about 269 hours in contrast to the initial IDX-SAHP. Besides, the environmental examination illustrates the use of IDX-SAHP in Beijing buildings can prevent the burning of 2,877, 000, 000 cubic meters of natural gas per year in the fossil fuel power plants and prevent the release of 8,055,600 tons of CO2 per year into the environment. Furthermore, the economical examination illustrates that Beijing residents may save approximately one billion USD per year on the purchase of electricity if they use the IDX-SAHP. Besides, the Chinese government can achieve about 109 billion USD per year if the equivalent electricity is exported instead of domestic consumption for Electric Water Heaters (EWHs). Moreover, the external costs decrease by 928 million USD per year if the IDX-SAHP is replaced with the EWHs. This paper discloses the significance of energy, economic, and environmental (3 E) examinations, optimization, and MODM during the IDX-SAHP design to increase the system efficiency and decrease the costs and environmental impacts by opting the most appropriate design parameters of the system.

Two-Layer Optimal Operation of AC–DC Hybrid Microgrid Considering Carbon Emissions Trading in Multiple Scenarios

To address the problem of low-carbon, optimal operation of AC–DC hybrid microgrids, a carbon trading mechanism is introduced and the impact of multiple uncertainties on system optimization is considered. Firstly, a two-layer model with the comprehensive economy of the hybrid microgrid as the upper layer and the respective optimal operation of the AC and DC sub-microgrids as the lower layer is established and the demand-side response is introduced, based on which the uncertainty of the scenery load is simulated using the multiscenario analysis method. Then, the baseline method is used to allocate carbon emission allowances to the system without compensation, and the actual carbon emissions of diesel engines, microcombustion engines, and fuel cells are considered to construct a hybrid microgrid. Finally, the model is solved using the CPLEX solver in conjunction with the calculation example, and the simulation verifies the effectiveness and feasibility of the proposed strategy in coordinating and optimizing the economy and low carbon of the system. The results show that when the carbon trading mechanism is considered, the carbon emission of the hybrid microgrid is reduced by 4.95%, the output power of the diesel generator is reduced by 5.14%, the output power of the fuel cell is reduced by 18.22%, but the electricity purchase from the power grid is increased by 38.91%. In addition, the influence degrees of the model considering the uncertainty of renewable energy and load are simulated. Furthermore, the impact of different electricity price models on optimal operation is evaluated, and the results show that electricity price will affect electricity purchase from the power grid and further affect carbon emissions.

Optimal Sharing Electricity and Thermal Energy Integration for an Energy Community in the Perspective of 100% RES Scenario

This paper presents a study on the optimal district integration of a distributed generation (DG) system for an energy community (EC) and the implementation of sharing electricity (SE) between users. In recent years, the scientific community has frequently discussed potential pathways to achieve a 100% renewable energy source (RES) scenario, mainly through increasing electrification in all sectors. However, cooling-, heat-, and power-related technologies are expected to play a crucial role in the transition to a 100% RES scenario. For this reason, a research gap has been identified when it comes to an optimal SE solution and its effects on the optimal district heating and cooling network (DHCN) allowing both electrical and thermal integration among users. The considered system includes several components for each EC user, with a central unit and a DHCN connecting them all. Moreover, the users inside the EC can exchange electricity with each other through the existing electric grid. Furthermore, the EC considers cooling storage as well as heat storage systems. This paper applies the Mixed Integer Linear Programming (MILP) methodology for the single-objective optimization of an EC, in Northeast Italy, considering the total annual cost for owning, operating, and maintaining the entire system as the economic objective function. After the optimization, the total annual CO2 emissions were calculated to evaluate the environmental effects of the different solutions. The energy system is optimized in different scenarios, considering the usage of renewable resources and different prices for the purchase of electricity and natural gas, as well as different prices for selling electricity. Results showed that, without changing utility prices, the implementation of SE allowed for a reduction of 85% in the total electricity bought from the grid by the EC. Moreover, the total annual EC costs and CO2 emissions were reduced by 80 k€ and 280 t, respectively.