Local Energy Networks Research Articles

Blockchain-Enabled Transformation: Decentralized Planning and Secure Peer-to-Peer Trading in Local Energy Networks

This paper introduces a novel blockchain-based automatic load response architecture for local energy networks, focusing on secure peer-to-peer (P2P) energy trading and decentralized planning. Departing from traditional centralized methods, the proposed system leverages non-cooperative game theory for pricing-based decentralized planning, enabling efficient resource distribution without a central authority. A key contribution is the integration of a machine-governed smart contract mechanism, which ensures secure, transparent, and consistent transactions in P2P energy trading. Additionally, an adaptive evaluation system for transaction nodes enhances the system’s responsiveness to dynamic energy demands. A distributed algorithm is developed to optimize the implementation of this architecture, ensuring practical efficiency. Case studies confirm significant improvements in operational efficiency, security, and economic outcomes, marking a substantial advancement in decentralized energy management. Key findings demonstrate that the proposed automatic load response strategy significantly enhances load curve stability, achieving a 99.16% reduction in net load fluctuations and an 8.24% reduction in operational costs compared to traditional methods. Additionally, the framework improves the self-consumption rate of renewable energy by up to 14.62% and reduces the average cost for electric vehicle (EV) users by 26.12%. These results highlight the framework's effectiveness in fostering a more balanced supply-demand relationship within local energy networks while ensuring economic and computational efficiency. The study underscores the potential to revolutionize decentralized energy management, offering a sustainable and cost-effective solution for future energy systems.

Expediting battery investment returns for residential customers utilising spot price-aware local energy exchanges

This paper presents a local energy exchange mechanism to benefit residential customers and retailers under variable wholesale market spot prices. To do so, a peer-to-peer (P2P) trading-enabled local energy market (LEM) is proposed to facilitate frequent energy transactions among participants. The proposed LEM mechanism considers market operational constraints and maximises electricity cost reductions. Further, the proposed LEM optimises the customers' investment return for battery energy storage systems (BESSs) and minimises retailers’ financial losses during higher spot prices in the wholesale market. Finally, a case study is demonstrated using real-world Australian data in which P2P trading is performed between interested buyers and sellers. Two scenarios with high and low wholesale market spot prices are analysed comprehensively considering five metrics such as solar-to-spot (STS); solar-to-load (STL); forward buy contract (FBC)-to-load (FBC-L); spot-to-load (Short); and FBC-to-spot (Long). The simulation results reveal that the customers reap substantial benefits from the proposed LEM in contrast with business-as-usual (BAU), allowing them to expedite BESS investment returns. Also, the proposed LEM helps a retailer decrease the effect of higher spot prices by reducing its unexpected financial losses while improving self-consumption and self-sufficiency in the local energy network.

Dependability of local power systems based on autonomous generation units

Aim. The paper aims to create and verify methods for predicting the dependability of autonomous power supply to local low-power consumers. Methods. The work uses simulation tools, i.e., computational experiment for predicting the dependability and safe service life of autonomous cluster distributed energy networks that include various power generation installations, power transmission lines and an automated self-regulation system for the purpose of diversifying the risks of failure. Results. Using methods of probabilistic analysis of technical system safety, the dependability of power supply is predicted in terms of possible energy network failures and threats to the life of consumers. The problem can be solved by simulating and predicting the moment of failure of a power network, i.e., critical violation of design conditions, accident, etc. This scenario allows – for long periods of operation – simulating failure situations due to degradation and destruction of elements, units, power lines, as well as failures under multidimensional uncertainty of prediction of dependability and quality of power supply. The key output of simulation is a set of recommendations to ensure energy security. The paper presents simulated dependability indicators, analysis of the effect of structural design solutions underlying a distributed energy network on the dependability and energy security. Conclusion. The use of computational experiments and results of power supply dependability simulation as part of predicting probabilistic indicators of failures of equipment and elements of cluster energy networks, identifying their consequences for the energy security of sparsely populated and remote locations lays the foundation for quality management of energy networks. Additionally, in the future, additional causes and initial events may be included in the model for the purpose of studying the facts of failures. For instance, such causes as uneven generation using renewable sources, finite fuel supply for conventional generation sources, reliability of the power grid personnel. The proposed approach allows verifying promising new energy network design solutions, for example, including energy storage systems into a distributed energy network that – at various points in time – may be either “generators” or “consumers”. Simulation of local energy networks is of practical interest and is important for improving their consumer quality, resistance to hazardous environmental effects.

Cyber-secure energy and flexibility scheduling of interconnected local energy networks with introducing an XGBoost-assisted false data detection and correction method

The growing penetration of renewable energy resources (RERs) and the ever-increasing implementation of information and communication technologies in smart grids necessitate the utilization of fast and safe operation methods. These schemes should be able to cope with the intermittency of uncertain RERs and fully explore the capabilities of new technologies while considering the vulnerabilities as a result of digitalization. Here, the integrity of the energy and flexibility scheduling and trading heavily depends on the security of the cyber-physical system. Local energy networks (LENs) are introduced to easily incorporate and utilize distributed energy resources, improving electrical power reliability and efficiency. This study develops a cyber-secure energy and flexibility scheduling platform for interconnected local energy networks. Hence, it introduces a novel false data detection and correction method based on the XGBoost machine learning method and investigates the performance of the proposed algorithm in the context of an interconnected local energy network and the corresponding energy and flexibility transactions. In this regard, this paper initially presents a bilevel energy and flexibility scheduling problem for interconnected local energy networks (ILENs) considering cyber-attacks (BLEFSCAILEN), which tries to satisfy the energy and flexibility requirements of the ILEN guaranteeing a resilient decision against false data injection cyber-attacks. Within the proposed BLEFSCAILEN a bi-level multi-objective optimization problem is defined where the operator of ILEN optimizes both energy and flexibility trading among LENs in the upper level to maximize its revenue. In the proposed structure, the operator of each LEN is situated at the lower level and tries to minimize the scheduling cost and simultaneously maximize the local flexibility index. Furthermore, in order to evaluate and improve the proposed system’s cyber-security, a false data injection attack is constructed using the Gaussian probability distribution function. Herein, the scheduling of ILEN is optimized considering vulnerabilities of electricity consumption data as a consequence of cyber-attack. Finally, the novel XGBoost-assisted deviation bound-based false data detection/correction (XGBFDD) algorithm is suggested to mitigate the above-mentioned cyber-attack and attain near real-world conditions. Simulation results demonstrate that the proposed false data detection/correction strategy exhibits a maximum accuracy of 91.67 precent in mitigating the attack effects. This is because the acquired post-correction results exhibit minor deviations in comparison to the original optimization output that is calculated with the pre-attack consumption data.

Alternatives for Transport, Storage in Port and Bunkering Systems for Offshore Energy to Green Hydrogen

Offshore electricity production, mainly by wind turbines, and, eventually, floating PV, is expected to increase renewable energy generation and their dispatchability. In this sense, a significant part of this offshore electricity would be directly used for hydrogen generation. The integration of offshore energy production into the hydrogen economy is of paramount importance for both the techno-economic viability of offshore energy generation and the hydrogen economy. An analysis of this integration is presented. The analysis includes a discussion about the current state of the art of hydrogen pipelines and subsea cables, as well as the storage and bunkering system that is needed on shore to deliver hydrogen and derivatives. This analysis extends the scope of most of the previous works that consider port-to-port transport, while we report offshore to port. Such storage and bunkering will allow access to local and continental energy networks, as well as to integrate offshore facilities for the delivery of decarbonized fuel for the maritime sector. The results of such state of the art suggest that the main options for the transport of offshore energy for the production of hydrogen and hydrogenated vectors are through direct electricity transport by subsea cables to produce hydrogen onshore, or hydrogen transport by subsea pipeline. A parametric analysis of both alternatives, focused on cost estimates of each infrastructure (cable/pipeline) and shipping has been carried out versus the total amount of energy to transport and distance to shore. For low capacity (100 GWh/y), an electric subsea cable is the best option. For high-capacity renewable offshore plants (TWh/y), pipelines start to be competitive for distances above approx. 750 km. Cost is highly dependent on the distance to land, ranging from 35 to 200 USD/MWh.

Edge-based solution for battery energy management system: Investigating the integration capability into the building automation system

Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.

Strategies for a Positive Anthropogenic Impact in Postwar Buildings

A significant portion of postwar buildings, typically concentrated in suburban areas, are now difficult assets to manage due to their poor sustainability and limited replacement feasibilities. This paper focuses on strategies to improve their metabolism using energy-saving measures based on optimizing energy needs and integrating internal and external energy sources: a new organizational model for energy management should focus first on saving energy, and then on the possibility of integration into a local energy network. This positively affects the anthropogenic impact and becomes a role model for aggregating buildings not only into a district system, but also into a wider, large-scale energy network. The paper shows a significant case study of actual retrofitting intervention that is examined in order to confirm the theoretical guidelines proposed in the first part of the paper. Moreover, another significant case study, taken from common practice, is illustrated, in which different levels of retrofitting are tested. While taking into account the complexity and fragmentation of private property both in a single building and in the city, some strategies are finally described with the aim of reducing the anthropic impact of the postwar building stock.

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

The increasing demand for distributed energy and active load increases the risk of voltage violations in active distribution systems. This paper proposes a distributionally robust transactive control method for local energy trading and network operation management of active distribution systems with interconnected microgrids. In particular, the emerging SOP technology is used for the flexible connection of the multi-microgrids (MMGs). First, the local energy interactive market between distribution network operator (DNO) and regional microgrids is constructed to simultaneously solve the economic and security problems of distribution networks considering the energy trading scenarios and various operational constraints. Then, the dual relaxation technique is used to transform the two-layer structure model into a single-layer model. Furthermore, the single-layer game model is further transformed into a two-stage distributed robust problem considering the uncertainties of load demand and renewable power outputs, and the second stage of the problem is decomposed into multiple parallel subproblems without dual information. Finally, numerical simulations on IEEE-33 test system verify the advantages and effectiveness of the proposed method.

A smart zero-energy building having bidirectional interaction with electricity/heating networks: An attempt to achieve a higher renewable penetration

The present research introduces an innovative zero-energy building complex equipped with a rule-based control approach for higher integration of renewable resources in the local energy network while bringing down energy costs. The idea centers on establishing several smart controllers to achieve a bidirectional interaction with the heating/electricity network for peak demand shaving and mitigate energy costs. The proposed system comprises Alkaline fuel cells integrated with a hydrogen storage tank driven by either a vanadium chloride cycle or an electrolyzer unit. The system also has an absorption chiller and smart thermal energy storage to supply the heating and cooling demands. TRNSYS-MATLAB developed code is applied to assess the system's indicators from techno-economic standpoints for a residential building complex in the Scandinavian climate. Also, the parametric investigation and time-dependent analysis are carried out to examine the impact of decision parameters and the ambient condition. According to the results, the solar system's physical appearance is very important since it significantly affects performance efficiency and total cost. The results further reveal that picking up the cells' current from 300 A to 500 A improves the performance efficiency by around 12% while lowering the total cost, illustrating the importance of optimization. The results highlight the importance of smart controllers by showing that over 70% of the year's net energy values are positive, indicating that the proposed system may meet demand and sell excess electricity+heating productions to regional networks. The results further demonstrate that since the net energy values are positive for the majority of days in the spring and summer, the system might operate more independently from the local energy networks on warmer days. Eventually, the higher share of solar in summer and wind energy in colder days for hydrogen production shows that the renewable resources combination results in a secure energy supply to obtain the highest independence from the local grid throughout the year.

A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.

An efficient renewable hybridization based on hydrogen storage for peak demand reduction: A rule-based energy control and optimization using machine learning techniques

The present study proposes and thoroughly examines a novel approach for the effective hybridization of solar and wind sources based on hydrogen storage to increase grid stability and lower peak load. The parabolic trough collector, vanadium chloride thermochemical cycle, hydrogen storage tank, alkaline fuel cells, thermal energy storage, and absorption chiller make up the suggested smart system. Additionally, the proposed system includes a wind turbine to power the electrolyzer unit and minimize the size of the solar system. A rule-based control technique establishes an intelligent two-way connection with energy networks to compensate for the energy expenses throughout the year. The transient system simulation (TRNSYS) tool and the engineering equation solver program are used to conduct a comprehensive techno-economic-environmental assessment of a Swedish residential building. A four-objective optimization utilizing MATLAB based on the grey wolf algorithm coupled with an artificial neural network is used to determine the best trade-off between the indicators. According to the results, the primary energy saving, carbon dioxide reduction rate, overall cost, and purchased energy are 80.6 %, 219 %, 14.8 $/h, and 24.9 MWh at optimal conditions. From the scatter distribution, it can be concluded that fuel cell voltage and collector length should be maintained at their lowest domain and the electrode area is an ineffective parameter. The suggested renewable-driven smart system can provide for the building's needs for 70 % of the year and sell excess production to the local energy network, making it a feasible alternative. Solar energy is far less effective in storing hydrogen over the winter than wind energy, demonstrating the benefits of combining renewable energy sources to fulfill demand. By lowering CO2 emissions by 61,758 kg, it is predicted that the recommended smart renewable system might save 7719 $ in environmental costs, equivalent to 6.9 ha of new reforestation.

Evaluating the Impact of Connectivity on Transactive Energy in Smart Grid

In future power systems, local energy networks (LENs) as one of the main infrastructures will have a key role in the realization of transactive energy (TE) exchange between involved players. In order to adopt this environment fully accessible to trade energy competitively between participants, the connectivity level of the network should be always retained. The connectivity is an attribute of the LENs which determines local network transitive capability (LNTC). This letter investigates the ways to widely implement the TE framework using the connectivity strength among prosumers, customers, and local networks. A methodology to derive the connectivity index for LENs is proposed based on the graph theory and algebraic approach in this letter. The proposed method could be considered as a supporting tool for TE management in future power systems. It is found that the strength of connectivity between the participants has direct influence on the quantity as well as quality of their energy transactions. It is considered that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> PMU data be used for on-line evaluation of the connectivity.

Automatic Demand Response Method for the Energy Storage Resource System Based on the Blockchain Technology Combined with Sensors

In order to improve the efficiency of the automatic demand response of the energy storage resource system, a user authentication and key agreement scheme for wireless sensor networks based on blockchain is proposed. The user does not need to register with the gateway node. As the light node of the blockchain network, the two can directly authenticate each other. The user obtains the public information of the sensor node from the blockchain through the client and verifies its legitimacy. It solves the problem that the traditional wireless sensor network user authentication scheme cannot be applied to the distributed Internet of things environment and the authentication process is low in security and efficiency. Mode 1 is the disordered charging mode. The charging facility provides continuous constant power charging service for the connected response subject until the user leaves. If the response subject is full before this, the charging will stop. Mode 2 is the BADR operation mode under charging optimization only, that is, the V2G function of PEV is not considered. In this case, 0 ≤ P l , k ≤ P l max c . Mode 3 is the BADR operating mode with V2G functionality in mind. The ELN load characteristics under the above three operating modes are discussed, respectively. Calculate and compare with economy on both sides of supply and demand. The experimental results show that the discharge compensation coefficient will not affect mode 2. In mode 3, as the compensation coefficient increases (from 0.4 to 1.2 at intervals of 0.2), the average total cost of the PEV cluster continues to decrease and the average total cost of the energy local network (ELN) system increases monotonically. Comparing the curves of the economic impact of the compensation coefficient on both sides of mode 2 and mode 3, it can be seen that the reasonable setting of the compensation coefficient is one of the key parameters for controlling the distribution of benefits. It proves that the proposed blockchain-based automated demand response (BADR) method can not only meet the satisfaction of energy demand but also coordinate the available energy storage resources in the system, so that the load level curve can track the output of new energy to the greatest extent and improve the balance between supply and demand of the system, which is suitable for a large-scale decentralized energy storage system.

Generalized Control of the Power Flow in Local Area Energy Networks

Local area energy networks (E-LANs) are cyber-physical systems whose physical layer is a meshed low-voltage microgrid fed by a multiplicity of sources, i.e., utilities, energy storage systems, and distributed power sources. The cyber layer includes distributed measurement, control, and communication units, located at end-user premises, as well as centralized supervision and dispatchment control. As compared with standard microgrid, the E-LAN encompasses the ability for end-users to actively contribute to the operation of the microgrid while acting as independent energy traders in the electrical market. Operational goals include active contribution of end-users to power sharing, loss reduction, voltage stability, demand response, fault identification and clearing, isolation of sub-grids for maintenance, islanding, and black start. Economic goals include the possibility, for each end-user, to decide in every moment, based on convenience, how his energy and power capacity is shared with other users, e.g., for demand response or to trade energy in the electric market. This paper introduces a comprehensive theoretical approach of E-LAN control to achieve all the above operational goals while providing a high level of dynamic protection against faults or other events affecting the system functionality, e.g., overloads or fast transients. It shows that meshed microgrids are the necessary infrastructure to implement the desired functionalities.

Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment

The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.

A highly innovative yet cost-effective multi-generation energy system for net-zero energy buildings

This study introduced an innovative yet feasible and cost-effective solution to make a big step forward in the state-of-art of smart energy buildings and obtain a real meaning of net-zero energy building. In this study, the main cornerstones of the developed solution combined the following technologies: the use of novel trigeneration solar collectors without a battery, a very clever method of heat pump integration with minimal size and cost required, and two-way interaction of the building with local energy networks. Different system configurations based on the included components were suggested and analyzed for an apartment building in Denmark. A thorough techno-economic and environmental evaluation of the proposed solution and the most competent ones already proposed for the same application was carried out to rank the best configuration from various facets. A comparative parametric study was accomplished to examine and compare the variation of performance indicators with significant decision variables. In addition, the tri-objective optimization was implemented for each configuration to specify the best optimum condition from energy, economic, and environmental standpoints. According to the economic results, the configurations integrated with battery and regular heat pumps were not favorable due to the highest total cost and the payback period. Here, the proposed system, owing to its resilient energy trade possibility with the energy networks and the scaled-down heat pump, gave larger energy-saving and CO2 emission reduction rates of 16.6% and 21.6%, respectively. The multi-objective optimization showed that the capacities of the battery and the cold storage were the most effective parameters on the performance of the system.

Environmental benefits through Storage, Exchange of thermal energy in smart city

The aim of this study is to look at the potential of a local sustainable energy network in a pre-existing context to develop a novel design beneficial to the environment. Nowadays, the concept of smart cities is still in the developmental phase/stage andwe are currently residing in a transitional period, therefore it is very important to discover new solutions that show direct benefits the people may get from transforming their city from a traditional to a smart city. Using experience and knowledge of successful projects in various European and non-European smart cities, this study attempts to demonstrate the practical potential of gradually moving existing cities to the level of smart cities by developing the available environmental resources. Data displays that using residual heat in a small neighborhood results in a lower annual gas consumption of at least 732. 200.00 m³, this incidentally leads to a reduction of CO₂ emission by 1,303,316 kg.

Techno-economic analysis and multi-objective optimization of a novel solar-based building energy system; An effort to reach the true meaning of zero-energy buildings

In the present work, a novel hybrid solar-based smart building energy system is introduced and studied. The system comprises innovative photovoltaic-thermal-cooling (PVTC) panels integrated with hot and cold storages with two-way interaction with electricity, heat, and cooling networks (if any). The proposed system is compared with PV-based systems integrated with battery and heat pump for a case study complex building in Aarhus, Denmark. The comparison is conducted by evaluating the performance and economic indicators and investigating the effect of significant parameters on each scenario via a parametric study. Furthermore, the optimal operating conditions and sizing of the proposed system are determined using the genetic algorithm method considering initial cost and traded energy with local energy networks as the objective functions. The comparison results show that the proposed solution is the most cost-effective scenario with the lowest initial cost of about 457,000 $ and a payback period of 6.6 years. This is mainly due to the simultaneous interaction with electricity/heat/cooling networks as well as the elimination of the battery and the heat pump, which are offered by the proposed scenario. It is shown that, in comparison to PV panels, the PVTC can produce 328.7 MWh and 125.6 MWh extra heat and cooling annually. The scatter distribution of significant parameters shows that the panel area and heat storage capacity are not sensitive parameters, and keeping the cold storage capacity at the lower bound is a techno-economically better option.

Analysis of the voltage drop of a drilling rig connected to a local energy network

The article discusses and analyzes the operation of an autonomous electrical complex of a drilling rig in an emergency mode, namely when the supply voltage drops. The concept of voltage drop is also described, a comparative characteristic of technical devices and methods of overcoming voltage drops, indicating the disadvantages and advantages, is given. Modeling of an autonomous electrical complex of a drilling rig is performed in the MATLAB Simulink software package with a description of the parameters of the main elements. The voltage drop is achieved by a sharp increase in the load power consumption, which in reality is achieved by the drilling rig head getting stuck in the hard layers of the earth’s surface. As a result, there are graphs of changes in the voltage value at the terminals of the electric motor of the drilling rig, depending on the magnitude of the load. Based on these graphs, we can conclude that with an increase in load, the autonomous system does not have its own reserves and power reserves (and according to regulatory documents, this margin is 20%) in order to overcome the voltage drop and return it to acceptable values for further uninterrupted operation. As a conclusion, we can say that today the only optimal option in terms of material costs and technical features is to connect a backup diesel generator connected in parallel to the main one and having a comparative power.

Principle design of regional energy optimal allocation

As the proposal of energy internet and intelligent energy, the utilization of energy and the energy categories has become more and more diverse. This paper redefines the concept of multi energy interconnection network, and divides local energy network, regional energy network and wide area energy network. In the planning and construction of energy interconnection network, the very important work is the optimal allocation of energy. In order to better allocate energy, we need to abide by specific principles, but there are few literatures on energy allocation principles. Taking the regional energy network as the research object, several principles of energy optimal allocation in the regional energy network are designed, and each principle is explained in detail. Finally, principles can provide reference for the planning and construction of regional men.