Multi Energy Research Articles

Optimal infrastructure in microgrids with diverse uncertainties based on demand response, renewable energy sources and two-stage parallel optimization algorithm

Future electric grids will face severe uncertainties with the unprecedented penetration of renewable energy sources, which may cause problems in grid exploitation. It is essential to evaluate the uncertainty of system performance in this grid; therefore, traditional exploitation methods may not be suitable for distributions grid such as multi-carrier microgrids when considering the electricity and gas grid constraints. This paper proposes an effective method for simultaneously optimizing different types of energy infrastructure in an environment with various uncertainties while considering grid constraints. To address the multi-energy synergy supply optimization problem in the micro energy grid, a coordinated two-stage programming approach is also suggested. A day-ahead and real-time phases are separated in the scheduling cycle to overcome the effects of the unpredictability of wind energy and solar power. The findings of the day-ahead forecast are then taken into account as uncertain variables for the higher-layer model. The demand response programming model and the energy storage revision model are lower-layer models considering the actual value as the realization of uncertain variables. The competitive swarm optimization algorithm is used to solve the problem mentioned above The Cumulative search optimization (CSO) uses global and local systems in particle swarm optimization and adopts a novel learning mechanism for creating competition between particle pairs. Comprehensive numeric examinations show that convergence speed and precision are critical, especially in solving large-scale problems. Therefore, we use chaos theory to improve its performance. Finally, the proposed method is tested and discussed on a system. The results showed that: (1) power-to-gas could convert extra electricity into natural gas; (2) the proposed two-stage optimization model and algorithm achieved different energy optimizations; (3) price-based demand response (DR) can level the energy load curve and maximize multi energy grid (MEG) revenue by using complementary specifications of energy price; and (4) the micro energy grid could communicate with the high-degree energy grid in order to gain more economic benefits. Accordingly, the incentive-based demand response (IBDR) output is reduced. The electricity and heating provided by convention gas turbine (CGT) is increased, which is fed to upper power grid (UPG) and upper heating grid (UHG). Since MEG’s purchasing power from UPG during the peak period generates more total revenue than island operations. Based on the obtained results, it can be seen that the proposed algorithm was about 50% faster compared to other methods and its standard deviation was about 0.0013 with different implementations, which was much better compared to other models.

Integrated demand-side management for multi-energy system based on non-cooperative game and multi-energy pricing

This paper proposes an integrated demand-side management (IDSM) approach based on non-cooperative game and multi-energy pricing strategy for a regional integrated energy system (RIES). On the demand side, the competition among multi-energy users was modeled as a non-cooperative energy consumption game, and the conditions to ensure the existence and uniqueness of the Nash equilibrium of the non-cooperative game were given. Considering the privacy of user information, a distributed multi energy consumption control algorithm based on dual theory and gradient descent method was designed to search for the optimal multi-energy consumption and the multi-energy daily retail price. In addition, for the energy supply side, the scheduling strategy of energy hub (EH) was proposed based on linear programming (LP) theory to optimize equipment operation. The results show that this approach can enable users and EH to coordinate optimization, so as to reduce the peak-to-average ratio (PAR) and energy cost of the system, and ensure the balance between supply and demand of multi-energy.

Energy Optimization Model of Multi Energy Interaction in Thermal Power Plants with Wind Power, Photovoltaic, Hydrogen Production and Hydrogen Fuel Cell System

It is proposed that an energy optimization model of multi energy interaction in thermal power plants with wind power, photovoltaic and hydrogen production and hydrogen fuel cell system (HPHFCS). Considering that the wind power, photovoltaic and HPHFCS are connected to the auxiliary power system of thermal power plant, according to the output characteristics of the multiple power sources and auxiliary power load, an energy optimization model of multi energy interaction at the auxiliary power is proposed and analysed. This paper quantitatively analyses the role of wind power, photovoltaic and HPHFCS connected to the auxiliary power system of thermal power plants in promoting the local consumption of renewable energy sources and reducing the power consumption rate of thermal power plants. Numerical cases and simulation results validate the correctness and effectiveness of the energy optimization model in this paper.

소수력발전 기반 그린수소 생산을 위한 수전해장치 및 에너지저장장치 최적 설비용량 산정

As renewable energy has been rapidly increasing, as renewable power outgrows load demand, power system reliability becomes a major issue. The ultimate solution for this issue is to store surplus energy without curtailment. Among the energy storage technology, P2G(Power-to-Gas) that convert and store surplus energy into hydrogen in MES(Multi Energy System) is the most resonable solution. In addition, it is more economical feasible when using electrolyzer connected hydro power plant. For stable electrolyzer operation, In this paper, we derive scheduling by considering the operating characteristics of the electrolyzer. And we estimate the optimal capacity of ESS, electrolyzer connected hydro power usint optimization. As a result of the simulation, the NPV(Net Present Value) is maximum when the capacity of the electrolyzer is 265 kW.

High-Pitch Multienergy Coronary CT Angiography in Dual-Source Photon-Counting Detector CT Scanner at Low Iodinated Contrast Dose.

The aim of this study was to evaluate the high-helical pitch, multienergy (ME) scanning mode of a clinical dual-source photon-counting detector (PCD) computed tomography (CT) and the benefit of virtual monoenergetic images (VMIs) for low-contrast-dose coronary CT angiography (CTA). High-pitch (3.2) ME coronary CTA was performed in PCD-CT in 27 patients using low contrast dose (30 mL of iohexol 350 mg/mL) and in 26 patients at routine contrast dose (60 mL). Low-energy-threshold 120 kV images (also known as T3D images) and 50 kiloelectron volts (50 keV) and 100 kiloelectron volts (100 keV) VMIs were reconstructed using a 1024 × 1024 matrix and 0.6-mm slices. The CT numbers, noise, and contrast-to-noise ratio (CNR) were measured in the ascending aorta (AA), left main coronary artery (LMCA), and distal left anterior descending (LAD) artery. Confidence in grading luminal stenosis with calcific plaque, noncalcific plaque, and stent was evaluated by 2 independent readers on a 0-100 scale (0 the lowest), and a CAD-RADS score was assigned. Image contrast enhancement, sharpness, noise, artifacts, and overall image quality were rated using a 5-point ordinal scale (1 the lowest). The radiation doses (CTDI) in low- and routine-contrast cohorts were 2.5 ± 0.6 mGy and 3.1 ± 1.7 mGy, respectively ( P = 0.12). At all measured locations, the mean CT number was >300 HU in 120 kV (LMCA 382.9 ± 76.2, distal LAD 341.0 ± 53.9, AA 399.5 ± 76.1) and 50 keV images (LMCA 667.5 ± 139.9, distal LAD 578.1 ± 121.5, AA 700.8 ± 142.5) in the low-contrast cohort, with a 96% increase in CT numbers for 50 keV over 120 kV. The CT numbers were significantly higher ( P < 0.0001) in 50 keV than 120 kV and 100 keV VMI. The CNR was also significantly ( P < 0.0001) higher in 50 keV than 120 kV and 100 keV images in all vessels. Confidence in the assessment of luminal stenosis in the presence of calcific plaque was significantly higher ( P = 0.001) with the addition of 100 keV VMI (median score, 100) than using 50 keV alone (median score, 70) and 120 kV (median score, 70) for reader 1, but no significant differences were seen for reader 2 who had same median scores of 100 for all image types. The confidence in the assessment of luminal stenosis within a stent improved with the use of 100 keV images for both readers (reader 1: median scores for 50 + 100 keV = 100, 50 keV = 82.5, 120 kV = 82.5; reader 2: 50 + 100 keV = 100, 50 keV = 90, 120 kV = 90). There were no significant differences in confidence scores for assessment of luminal stenosis from noncalcific plaques for both readers. The reader-averaged qualitative scores for vascular enhancement and overall image quality were significantly higher for 50 keV VMI than for 120 kV images in both low- and routine-contrast dose cohorts. The image sharpness was nonsignificantly higher at 50 keV VMI than 120 kV images, and the artifact score was comparable for 50 keV VMI and 120 kV images. The noise was higher in 50 keV VMI than in 120 kV images. High-pitch ME PCD-CT mode produced diagnostic quality coronary CTA images at low radiation and iodinated contrast doses. The availability of ME VMIs significantly improved the CNR, overall image quality, and confidence in assessment of luminal stenosis in the presence of calcific plaques and stents, and resulted in change of CAD-RADS categories in 9 patients.

Optimal operation strategy of wind-hydrogen integrated energy system based on NSGA-II algorithm

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has a better economy and higher energy efficiency.

Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm

As a basic industry for national economic development, the power industry is closely related to the overall economic and environmental development of China. At present, China is still dominated by thermal power generation. In order to reduce carbon emissions, promote the realization of the “double carbon” goal, and improve the level of clean energy utilization and the operating efficiency of the power system, a wind-light-water storage complementary power generation system is built, and a mathematical model of multi energy complementation is established. The minimum economic cost and the minimum battery capacity are proposed as the objective functions of system capacity configuration. Then a multi-objective evolutionary algorithm based on Pareto optimal space of the NDWA-GA and the PCA is proposed for optimal capacity allocation of multi energy complementary systems in this paper. Compared with the traditional multi-objective optimization algorithm, the correctness and effectiveness of the proposed method are verified. In addition, according to the actual research object, the optimal capacity configuration of the multi energy complementary system is given, which can guide the production and has an important promotion significance for energy saving and emission reduction.

Including thermal network operation in the optimization of a Multi Energy System

The combined production of different energy vectors with Multi Energy Systems is a very attractive opportunity to increase the generation efficiency, compensate the oscillations of renewable sources, and improve the flexibility in power generation. Optimizing their operation is a complex task, since the problem can easily reach high dimensions, representing a challenge for commercial solvers. The inclusion in the optimization of a thermal network whose simulation is based on temperatures and flowrates allows to significantly improve the applicability of the obtained results. In addition, the effect of the operating temperatures on the performances of thermal components should be included as well. With these purposes, the present study proposes a strategy for the operation optimization of a MES and its internal thermal network. The model relies on a decomposition approach, where the original problem is divided in two subproblems. In the first one, the MES operating costs are minimized without considering the effects of the thermal network, while in the second one, the thermal network operation is optimized in order to find the operating conditions that are more favourable to the ones found for the MES. These subproblems are iteratively solved until the process converges to a stable solution. Some efforts are taken to keep the mathematical formulation as simple as possible (the MES is a Mixed Integer Linear Programming, while the heating network is a Quadratically Constrained Programming). The developed model allows to find near-optimal solutions which satisfy the numerous physical and technical constraints addressed. The results provide an optimized schedule for the thermal storage in terms of mass flowrates and temperature. One of the strengths of the model is the relatively low computational time required to reach the convergence and, despite not being the global optimum, the high quality of the solution obtained.

Application prospect and key technologies of digital twin technology in the integrated port energy system

Introduction: The structure of green and low-carbon ports is complex, and an integrated port energy system with complex characteristics such as multi-energy flow coupling and information physical fusion has been formed.Methods: Digital twin technology provides a new solution for the perception, analysis, and optimization of integrated port energy system. The paper explores the digital twin of integrated port energy system. Based on the development trend and function of integrated port energy system, the digital twin of integrated port energy system architecture is proposed to explore its potential application services. Specifically, considering the functions and structural characteristics of the port, the digital space of integrated port energy system is established, which describes the pan electrification, multi energy flow fusion, the “physics-information-value” coupling, and the characteristics of system operation under uncertain scenarios of integrated port energy system from the physical and data dimensions. The hybrid driving method based on data and mechanism is used to explore digital twin of integrated port energy system and find potential better applications.Results and discussion: Finally, combined with the current development needs of integrated port energy system, this paper analyzes the key problems and technologies that need to be broken through in the specific implementation of digital twin of integrated port energy system, with a view to providing some thoughts and references for the low-carbon development of ports and the future application of digital twin technology under the goal of “double carbon”.

Time Series Optimal Capacity Determination Model of Regional Comprehensive Energy Considering Multi Energy Complementarity and Load Flexibility

In order to improve the voltage stability margin and convergence speed of the power grid, a regional comprehensive energy time series optimization capacity model is established under the condition of fully considering the multi energy complementarity and load flexibility. The multi energy complementary optimal operation model of regional comprehensive energy system is established, the load flexibility and multi energy complementary characteristics are analyzed, and the robust optimal capacity target of regional comprehensive energy system is set on the basis of considering the load flexibility. From the perspective of time series, an optimized capacity model is constructed, and genetic algorithm is improved to solve the multi-objective optimization model of regional comprehensive energy time series capacity. The experimental results show that this method has good optimization effect, improves the stability margin and convergence speed of power grid voltage, and can find the optimal solution for the established objective function, which effectively improves the effect of capacity determination.

Energy Hub Optimization on Residential Building Case

The growth of the energy consumed, have led to the need of upgrading and restructuring the operation of existing energy systems in the building sector. In context, goals of reducing the primary energy consumption based on fossil fuels and the limitation of carbon dioxide (CO2) emissions to the atmosphere, were set, in order to enhance the provision of affordable and generally clean energy for the citizens. In addition, the European Union (EU) promotes the utilization of energy systems based on Renewable Energy Sources (RES). Αll above highlight the importance of the decision-making process during the design phase of a building. For these reasons, this paper deals with the optimization of multi energy systems, introducing the concept of the Energy Hub, in order to cover the thermal demands of a residential building located in Thessaloniki (Greece). The proposed methodology includes the calculation of the building energy demands on a monthly basis via the simulation program FineGREEN19. Afterwards, a Mathematical Programming model was constructed, in order to provide the optimization scheme of multi energy systems, considering different criteria. The criteria include the minimization of economic, energy and environmental aspects, considering the concept of Life Cycle Assessment (LCA). The General Algebraic Modeling System (GAMS) was used to model the optimization problem. The results of Single Criteria Optimization problem figure out the contradictory between the criteria, showing that the use of the optimization models can improve and facilitate the building design.

Planning Model for Integrated Energy Supply System in Park Level Regions Under the Energy Internet

With the reduction of traditional fossil fuels and the increasing severity of environmental issues, it is of great significance to study energy system planning and optimization models that complement and integrate multiple energy utilization methods in the context of the energy internet for building an integrated energy supply system. Firstly, this article divides the planning indicators of the regional integrated energy supply system into four categories based on the goal of “two highs and three lows”; Secondly, analyze the three key issues of exergy efficiency, economy, and multi energy coupling in regional integrated energy planning; Finally, a multi-objective planning model for regional integrated energy systems that takes into account equipment capacity planning and operation scheduling optimization is proposed, with the optimization objectives of minimizing the annual value of full life cycle cost and maximizing efficiency, and a double-layer optimization structure is designed for efficient solution.

Virtual smart energy Hub: A powerful tool for integrated multi energy systems operation

The present work introduces a novel unit, named virtual smart energy hub (VSEH). VSEH consists of a combination of smart energy grids, smart energy hubs (SEHs), and virtual power plants (VPPs) and simultaneously benefits from the advantages of SEHs and VPPs on a smart-energy-grid platform. A comprehensive two-stage model was proposed to solve the VSEH optimal operation problem in order to trading in day-ahead and intraday heat and power market. The proposed stochastic programming model is risk based and the conditional value at risk (CVaR) index was employed to represent the risk in the scheduling model. On the first stage of the proposed model, VSEH schedules transactions amounts by considering energy prices and SEHs constraints in order to maximize profits from participation in day-ahead heat and power markets. In the second stage, VSEH, through the more accurate prediction of uncertainties before operation time and with the aim of maximizing profits and correcting deviations relative to the settled transactions in the first stage, re-schedules the SEHs in intraday heat and power markets. Non-arbitrage constraints were established between day-ahead and intraday energy markets in the proposed model. Moreover, the presented structure was modeled and comprehensively defined in mathematical terms as a mixed-integer linear programming (MILP) model with a minimum number of real variables. The performance of this model was evaluated by implementing it on a coalition with various SEHs, as a result of which the profitability of the coalition and the increase in the risk tolerance level of the SEHs group was confirmed. The results indicate that formation of the VSEH can act as means of risk management in addition to reducing risk-associated costs regardless of the circumstances on the day of operation and the acceptable level risk tolerance of the SEHs. In other words, the integration of SEHs will result in a lower risk aversion cost.

Carbon-free hydrogen and bioenergy production through integrated carbon capture and storage technology for achieving sustainable and circular economy– A review

Drastic changes in climatic conditions accelerate the need for the implementation of emission reduction techniques to restrict global warming. Conventional technologies do face severe drawbacks in reducing carbon emissions. Carbon neutrality is considered as the most essential concept as it can alleviate the issues of greenhouse gas emissions while deriving energy and fuels from fossil origin. Several reduction technologies are proposed in the recent past, in which the integration of renewable energy resources such as solar and wind power are signified. The review aims to provide an understanding of the technology transition to attain carbon neutrality through production of carbon-free energies such as hydrogen and bioenergy. Despite the chemical and physical sequestration methods, the biological sequestration technique is given special attention for the mitigation of CO2 emissions by utilizing photosynthetic microalgae. Of numerous microalgae, Chlorella vulgaris, a common eukaryotic microalga found in freshwater showed a maximum CO2 fixation rate of about 3.35 g/L/d with 10 % CO2 input. Apart from capturing and storing carbon in the geological site, the concept of utilizing and converting carbon into valuable byproducts is described elaborately. The possibilities for utilizing renewable energy sources for carbon-free energy production through unique prototypes are reviewed and elaborated. It mainly aids to utilize renewable resources more than conventional, which significantly minimizes the carbon footprint. The integration of bioenergy, carbon capture and storage concepts with renewable energy sources extends opportunities to minimize emissions and transition towards carbon neutrality and indeed can aid in achieving carbon negativity. The aforementioned concept was reported to produce about 12.5 million tons ton of hydrogen with 133 million tons of CO2 recovery. An innovative prototype to resolve emission issues other than CO2 was also addressed. Illustrations of the artificial photosynthesis process, convective vortex system and integrated multi energy system are disc proposed innovative carbon reduction technologies' economic feasibility and environmental outlook technologies are reviewed and presented.

Multi-objective risk-constrained optimal performance of hydrogen-based multi energy systems for future sustainable societies

Hydrogen is one of the leading energy sources under special consideration due to its sustainability and technological development. The main concept of integrated energy system is applied in this article. The goal is to maintain a coordination between hydrogen cycle where hydrogen is produced and converted with renewable energy (RE). Three energy storage for charging/discharging hydrogen, hot and cold water are employed enhance the energy efficiency. The demand-side energy system could rely on this combination that enables the increase in wind and solar energy utilization and uses it to generate hydrogen in future smart cities. This paper uses a scenario-based stochastic optimization to model the associated uncertainties. The epsilon-constraint and max-min fuzzy methods are used as multi-objective approach to satisfy environmental and economic goals, simultaneously. To analyze the associated risk imposed by multiple uncertain parameters, the downside risk constraint (DRC) is implemented to render several risk-averse strategies to the decision-maker based on various constraint level. The results show that the final solution in risk neutral strategy satisfies both emission and operation costs. The ultimate associated risk in conservative strategy will tend to zero at the 4.2% operation cost increment and 3% emission reduction.

Active-reactive power collaborative optimization model of electrical interconnection system based on deep learning under the goal of “carbon neutrality”

With the goal of carbon neutralization put forward, new energy units have gradually become the main power supply, and its volatility has brought great challenges to the control and optimization of the system, especially the distribution network. In order to solve the reactive power problem caused by the high proportion of renewable energy connected to the distribution network, a source network load storage centralized optimal dispatching model considering reactive power optimization and multi energy collaborative interaction is proposed. The model aims at the optimal operation cost, minimum network loss and minimum carbon emission of the multi energy system, and considers reactive power compensation, energy storage regulation and energy conversion, so as to realize the safe and low-carbon economic dispatching of the electrical interconnection system. The switching capacity of the discrete reactive power compensation device is linearized, and the model is transformed into a mixed integer second-order cone programming problem for solution. The simulation results show that the proposed method can effectively compensate the reactive power required by the wind turbine parallel network, coordinate the energy interaction between electricity and gas, and improve the stability and flexibility of the distribution network after large-scale renewable energy access, According to the charging and discharging power and charge state, and the automatic adjustment of the gears of on load voltage regulating transformer and compensation capacitor bank, this paper shows that the system has safe and stable operation performance.

Lp-CMFD acceleration schemes in multi-energy group 2D Monte Carlo transport

The linear prolongation flux update scheme is extended to both regular CMFD acceleration, as well as partial CMFD acceleration in 2D multi energy group Monte Carlo k-eigenvalue neutron transport problems. The acceleration performance of these CMFD variants were investigated in simple 2D slab geometries, first with a monoenergetic case and then with a three group problem on the same geometry based on the monoenergetic cross sections. Flux convergence was determined via an on-the-fly convergence diagnostics developed by Ueki and Brown. It is found that on top of providing better acceleration in general, the linear prolongation scheme is also able to correct for instabilities in the CMFD scheme. Overall, the lp-pCMFD scheme employing a maximum history length is found to have the best performance across the cases presented.

Preventive scheduling of a multi-energy microgrid with mobile energy storage to enhance the resiliency of the system

In multi energy microgrids with renewable energy sources, the significance of consideration of the gas supply network and efficient interaction between various energy carriers has been developed. Also, one of the most consequential matters linked with the operation of microgrids is affording the resiliency of the microgrid in inconstant conditions such as natural disasters. In this paper, the process of estimating resiliency in multi-energy microgrids is presented and the application of mobile energy providers is believed to prevent the devastating outcomes of natural disasters and improve the resiliency of the system. The methodology of this article is based on cost of not supplied energy for devastating effects of natural disasters on multi energy microgrids. Several scenarios have been presented to model the destructive consequences of natural catastrophes in the multi-energy microgrid. Different energy providers have been investigated to obtain the most desirable solution according to geographical conditions and demand characteristics. One of the important tools adopted in the multi-energy system to compensate for the resiliency of the system is mobile energy providers. According to the simulation outcomes, the value of the resiliency criteria and the total cost are improved due to the different scheduling strategies based on the application of mobile energy providers.

Strategy optimization of district energy system in case of sudden demand response or power accident

With the wide application of regional centralized cooling (heating) system in the world and the growing maturity of energy supply technology, the focus of system optimization gradually transforms from technical optimization to strategic optimization to attain the common goals of economy and energy saving. Aiming at the operation strategy model of power grid interactive district energy system based on time-of-use (TOU) price proposed in previous research, this paper proposes new objective equations and constraints to explore the strategic deployment of district energy system when accepting the power grid requirements to reduce or increase the power load directly to improve the economy and energy efficiency of centralized cooling (heating) system. The modified optimization strategy model is used to respond to sudden demand response or power accidents. The strategy model can be directly controlled by the power grid, and the TOU price is no longer the main factor affecting the formulation of the strategy. In the face of sudden demand response or power accidents, the operation strategy optimization model proposed in this paper can guarantee the energy supply demand of users and optimize the economic benefits and energy efficiency of the systems. The operation strategies under various optimization objectives can be obtained through the simple input of energy loss of the pipeline and user cooling or heating load. The proposed model is suitable for the load allocation of energy system with single energy supply form and for the strategy optimization of multi energy coupling system. The accuracy and validity of the proposed model under various optimization objectives are verified based on experiment and simulative research.

Assessment of hydrogen-based long term electrical energy storage in residential energy systems

Among the numerous envisioned applications for hydrogen in the decarbonisation of the energy system, seasonal energy storage is usually regarded as one of the most likely options. Although long-term energy storage is usually considered at grid-scale level, given the increasing diffusion of distributed energy systems and the expected cost reduction in hydrogen related components, some companies are starting to offer residential systems, with PV modules and batteries, that rely on hydrogen for seasonal storage of electrical energy. Such hydrogen storage systems are generally composed by water electrolysers, hydrogen storage vessels and fuel cells.The aim of this work is to investigate such systems and their possible applications for different geographical conditions in Italy. On-grid and off-grid systems are considered and compared to systems without hydrogen, in terms of self-consumption ratio, size of components and economic investment. Each different option has been assessed from a techno-economic point of view via MESS (Multi Energy Systems Simulator), an analytical programming tool for the analysis of local energy systems.Results have identified the optimal sizing of the system's components and have shown how such systems are not, in general, economically competitive for a single dwelling, although they can in some cases ensure energy independence.