Search

With the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable‐based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price‐based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval‐based formulation. The developed methodology incorporates weather, load, energy pricing and plug‐in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Power‐Flow (PF) solution in isolated microgrids has attracted notable attention recently, because these systems present various particularities compared with the traditional PF solution in large meshed transmission networks. In this sense, this paper develops a novel Newton‐like PF solver for isolated microgrids. The new proposal is based on the Modified Midpoint method and shows high convergence order with relatively low computational burden. These characteristics bring superior theoretical performance compared with the standard Newton–Raphson (NR) method and other high order techniques, which has been conventionally used. Extensive simulations are performed on various small‐, and large‐scale benchmark Microgrids under different loading conditions and R/X ratios. Results provided serve to confirm the theoretical features of the developed solver, outperforming the NR technique as well as other recently developed solvers in all the studied systems with acceptable reliability even under high stressed conditions. [ABSTRACT FROM AUTHOR]

Hydrogen is called to play a vital role in the future decarbonization of the electricity industry. Among its multiple applications, this energy carrier may improve the energy storage, replacing or complementing the traditional battery banks thanks to its higher energy density. However, the low efficiency and cost of associated devices as well as the difficulty in transport make unfeasible the implantation of hydrogen storage systems at the residential level. However, emerging paradigms like energy communities may change this concept making viable the installation of hydrogen chains in the domestic sector. This paper focuses on day-ahead scheduling of energy communities with integrated collective hydrogen storage system. To this end, a three-stage methodology is developed in which the first level is focused on individual home energy management, the second level handles with peer-to-peer energy trading among prosumers and the last level determines the energy exchanging profile with the utility grid accounting with the hydrogen chain. To handle with uncertainties from renewable sources, demand and energy price, the Information Gap Decision Theory (IGDT) is employed, by which an uncertainty-aware scheduling program can be obtained minimizing the negative effects of uncertain parameters. A case study is performed on a six-prosumer energy community with electrolysis, hydrogen vessel and fuel-cell, allowing both purchasing and selling energy with the grid. The results serve to prove the effectiveness of the developed methodology as well as demonstrate the possible impact of unknowns in energy community operation, and how the hydrogen chain can help to improve the economy and self-sufficiency of the system. • A three-level operating strategy for ECs is proposed. • A multi-stage methodology for day-ahead scheduling of ECs with collective HSS is developed. • Uncertainties from renewable generation and demand are treated using IGDT. • The impact of uncertainties in the operation of ECs is discussed. • The role of HSS in improving the economy of the community is highlighted. [ABSTRACT FROM AUTHOR]

Hydrogen is called to be one of the most important energy vectors in future energy systems. Nowadays, its use in the industry sector is prominent, finding multiple applications in fertilizer production or oil refining. In this sense, some industry sectors demand a considerable amount of hydrogen for their processes. In many cases, hydrogen must be purchased externally, which supposes a challenge due to hydrogen transportation is costly and few efficient. In this context, local hydrogen production through mature electrolysis technology may suppose an attractive alternative in industrial parks. This paper focuses on this topic, in particular, a risk-averse electrolyser sizing methodology is developed. The new approach accounts for uncertainties in electricity prices as well as local renewable generation and demand through an original hybrid stochastic-robust model. The developed uncertainty modelling is integrated into a novel four-level optimization framework, whose main result is the optimal electrolyser rated power. To efficiently attain the solution, an original hybridization of the Benders' decomposition and the Column and Constraint Generation Algorithm is proposed. The developed methodology results efficient in an illustrative three-industry park, showing that installing local hydrogen generation may reduce the amount of hydrogen purchased externally (by 38%) and project costs by 2.5%. Furthermore, increasing the robustness level leads to increase the project cost by 8%, while assuming unfavourable realization of uncertainties. Moreover, the developed tool is further validated in larger parks, involving an increasing number of industries, showing that the proposed methodology scales well with the size of the park. • A novel risk-averse methodology for electrolyser sizing is developed. • The new tool is conceived for industrial parks but adaptable to other systems. • Project costs can be reduced by 2.5% when local hydrogen production is enabled. • The robustness level impacts incrementing the total expenditures by 8%. • The developed methodology scales well with the size of the system. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

Growing demand, interconnection of multiple systems, and difficulty in upgrading existing infrastructures are limiting the capabilities of conventional computational tools employed in power system analysis. Recent studies manifest the importance of efficiently solving well- and ill-conditioned Power-Flow cases in a modern power-system paradigm. While the well-conditioned cases are easily solvable using standard methods, the ill-conditioned ones suppose a challenge for such solvers. In this regard, methods based on the Continuous Newton's principle have demonstrated their ability to address ill-conditioned cases with acceptable efficiency. This paper demonstrates that the approaches proposed so far do not extract the best numerical properties of such solvers. To fill this gap, an optimization framework is proposed by which the parameters involved in the two-stage Runge–Kutta-based solvers are appropriately set, so that the stability and convergence order of the numerical mapping are maximized. By using the developed optimization technique, three solvers with quadratic, cubic, and 4th order of convergence are developed. The new proposals are tested on a variety of large-scale ill-conditioned cases. Results obtained were promising, outperforming other conventional and robust approaches. [ABSTRACT FROM AUTHOR]

The decarbonization of the mobility sector motivates to increase the penetration of battery and fuel-cell electric vehicles. The proliferation of these mobility modes will be accompanied by the massive installation of charging and refilling infrastructures into existing networks. This work focuses on fuel-cell vehicles, for which refilling points are needed. Difficulties in hydrogen transportation can be circumvented by deploying onsite hydrogen generation assets (electrolysers) and storage, which can be partially or fully supplied through local renewable generators. Nevertheless, such assets require a considerable initial investment, being necessary the use of planning tools in order to maximize revenues for private investors. This paper focuses on this issue. In particular, an optimal sitting and sizing tool for hydrogen refilling stations with onsite storage and electrolysers is developed. The developed methodology considers the influence of locational marginal prices, which are cleared by the distribution system operator in order to translate the real electricity cost per node. This pricing strategy helps to best allocate assets through the network and thus resulting valuable for planners in order to site refilling infrastructures properly. An original multi-year iterative algorithm based on the multi-cut Benders' decomposition is proposed in order to alleviate the intrinsic high computational cost of the planning tool while accommodate long-term inflation and degradation rates of parameters. A number of simulations are performed on the well-known IEEE 33-bus system. Results verify that locational marginal pricing effectively translates the nodal electricity cost to end-users. Remark, the total electrolysis capacity turns out to be the most significant parameter, reducing further the cost of the project, while storage capacity has a limited influence. Results highlight the importance of the infrastructure capacity when determining the placement and sizing of electrolysers, thus supporting decisions when upgrading existing infrastructure. The impact of the number of stations to be installed and the budget cap is also analysed, showing that both parameters have similar influence and may reduce the total project cost by 70% approximately. The typical scheduling behaviour of the electrolysis-storage facilities is discussed, showing how storage is capable to provide energy arbitrage exploiting locational marginal prices. Finally, the computational performance of the developed algorithm is assessed, verifying that the new tool is efficient and portable. [Display omitted] • A new tool for sitting and sizing refilling stations in distribution networks is developed. • Local marginal prices are considered. • A multi-year iterative algorithm is developed for efficiently solving the problem. • Local marginal pricing translates nodal electricity costs and helps to allocate assets. • The installed electrolysis capacity is the most influence parameter in reducing costs. [ABSTRACT FROM AUTHOR]

Nowadays, millions of people in remote areas do not enjoy an uninterrupted power supply due to the lack of connectivity to the main power grid. Under such circumstances, the only feasible way to access electricity is typically local power generation, often relying on diesel engine–generator sets or photovoltaic arrays. However, on many occasions, this configuration does not fully exploit all available local resources, including biomass. Indeed, most isolated areas have access to local biomass production from agricultural activities, which can be used for local electricity generation through gasification. This paper addresses this challenge by developing an innovative optimal sizing tool for hybrid power plants integrating biomass gasifiers, specifically designed for isolated areas with access to local biomass production. The novel approach models the particular features of biomass gasification technologies, including long on/off times or restrictive ramping limits. To this end, an efficient methodology based on representative weeks is proposed, which is combined with a solution strategy based on the multi-cut Benders' decomposition, thus resulting in a tractable framework that can deal with a huge amount of data efficiently. One of the most salient features of the new proposal is the consideration of local biomass production, which is included in the methodology through an original algorithm. Accordingly, a certain amount of biomass is sourced locally, leading to more accurate and reliable results. The new methodology is applied to a benchmark off-grid community in Ghana. The results demonstrate that the use of gasifiers reduces the project cost notably (by 90%) driven by the reduced biomass cost, which can be supplemented by locally generated biomass from agricultural activities. In addition, this technology constitutes a clean source of energy, reducing the total CO 2 emissions by 83% compared to a baseline case in which only diesel generators are used. Moreover, it is demonstrated that biomass gasification can effectively act as base load power generation technology to reliably cover most of the local demand, thereby enabling a clean and inexpensive dispatchable local power generation. Finally, a sensitivity analysis reveals that the economic feasibility of the plant is more sensitive to the biomass cost than the selling price of biochar, resulting in a 33% increment in the total project cost when the price of biomass increases from 0 to 0.4 $/kg. Nevertheless, gasification remains as the predominant power generation technology even under unfavorable prices. • A novel optimal tool for sizing hybrid plants with biomass gasifiers is developed. • The representative weeks' approach enables efficient data handling. • Multi-cut Benders' strategy is adopted to deal with extended time horizons. • Integrating biomass gasifiers in off-grid communities cuts project costs by over 90%. • Biomass gasification acts as base load power generation to cover most of the local demand. [ABSTRACT FROM AUTHOR]

The proliferation of charging stations entails multiple challenges for power systems. In this regard, the installation of photovoltaic-battery systems may help to mitigate the negative effects of charging points. However, such assets should be carefully planned, paying attention to economic aspects, principally. Most of existing works optimize the photovoltaic-battery system in charging infrastructures taking a representative-space of the involved variables (e.g. photovoltaic potential, charging demand or energy prices). However, this approach tends to ignore low-probable scenarios. Thus, the best-case scenario for charging demand (i.e. that for which the highest charging profit is accessible) may not be included in the analysis and therefore such demand could be not attended properly, thus losing this monetary opportunity. This paper focuses on this issue and questions if considering the best-case scenario into planning photovoltaic-battery systems for charging stations is worthwhile or not. To this end, a novel best-case-aware planning tool is developed, including the best-case scenario through a novel chance-constrained formulation. The overall problem is then decomposed into a master-slave structure by which the economy of the system is optimized together with the number of scenarios for which the best-case profile can be attended. A case study serves to validate the developed tool and shed light on the questions arisen in this work. In particular, it is checked that considering the best-case scenario into planning tools is questionable from a monetary point of view. Nevertheless, its inclusion unlocks some collateral advantages such as incrementing the users' satisfaction or reducing the grid-dependency. [ABSTRACT FROM AUTHOR]

Hydrogen is called to play a vital role in the decarbonization of the energy sector. Its importance is notable for industries, where it finds multiple applications, but also for electricity generation through fuel-cells. Due to the existence of mature conversion technologies, the electricity‑hydrogen nexus is expected to be frequently present in the future for multiple applications. In this sense, it is very important to develop computational tools that consider both energy vectors and their links. Following this idea, this paper develops a local electricity‑hydrogen market model for industrial parks. The new model is suitable for systems with a notable presence of hydrogen consumers and on-site hydrogen generation through water electrolysis. The proposed market framework is capable to jointly reveal local electricity and hydrogen prices without ignoring the strong physical and financial interactions between systems. The mathematical notation arises from Stackelberg-based models for the electricity and hydrogen pricing sub-problems, which are solved iteratively until reaching convergence, thus resulting in a holistic optimization framework solvable by average machines. A case study is presented with results, which serve to validate the new tool as well as analyse several important aspects such as the role of fuel-cells or local renewable generation. Moreover, the results obtained allows us to show how both sub-systems are capable to cooperate seeking the collective welfare, thus augmenting the possibilities and flexibility of the whole system. • A novel local electricity model for industrial parks is developed. • The different agents involved are properly modelled. • Electricity and hydrogen prices can be calculated. • Electricity and hydrogen systems can cooperate seeking collective welfare. • The presence of photovoltaics or fuel-cells may notably impact on results. [ABSTRACT FROM AUTHOR]

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities. • Load profiles and electricity consumption datasets are valuable instruments for a better understanding of demand dynamics. • Analysis of appliance behaviour patterns helps to support and optimize turn-key DSM systems. • Controllable appliances' patterns contribute to energy savings and peak load reduction. [ABSTRACT FROM AUTHOR]

Industrial parks allow industries to share infrastructure and thus saving money, finally redounding in improving the economy of many countries worldwide. Given the objectives of carbon neutrality imposed by different entities, it results mandatory promoting energy efficiency in industrial parks. Aligning with such objective, encouraging industries to provide energy flexibility becomes essential. In the electricity sector, such flexibility can be provided through optimally managing local assets such as energy storage and flexible loads. However, flexibility provision should be promoted by implanting proper pricing mechanisms. This paper focuses on this issue by developing a local market clearing mechanism for industrial parks, whose main novelty redounds in the inclusion of a fair pricing mechanism through which industries are paid by flexibility provision. Different types of flexible loads are considered and modelled (i.e. curtailable, interruptible and deferrable), so that the new proposal is suitable for leveraging fully capabilities of industrial flexible loads. The whole pricing mechanism is raised as a bi-level game-based model, by which local energy and flexibility prices are revealed in a coordinated way. Challenges brought by the inclusion of binary variables (needed for modelling some types of flexible loads) are solved by proposing a solution algorithm based on the well-known Column & Constraint Generation Algorithm. The resulting optimization framework is Mixed Integer Linear Programming, being therefore solvable by off-the-shelf solvers. A case study is presented to validate the new proposal as well as highlight some important aspects related to local markets in industrial parks and its practical implantation. • A new market model for electricity provision in industrial parks is proposed. • Different flexible loads are modelled including binary variables. • Binary variables are accommodated in a developed Benders-based algorithm. • Local storage has a direct impact on shaving peak load prices. • Renewable generation may discourage to partake in flexibility provision. [ABSTRACT FROM AUTHOR]

With the increasing electricity consumption and difficulty in upgrading existing infrastructures, ill‐conditioned power flow (PF) cases are becoming more frequent nowadays. In this context, classical robust solvers may be unsuitable for realistic networks, which typically encompass thousands of buses, because of their high computational burden or low convergence rate. This article tackles this issue by proposing a novel PF solver, which presents acceptable robustness and efficiency in solving large‐scale ill‐conditioned systems. The proposed algorithm collects the advantage of various numerical solvers, which by separate present different weaknesses, but actuating in coordination their strengths can be jointly exploited. More precisely, the robust Forward‐Euler and Trapezoidal rules are combined with the efficient Darvishi cubic technique. Thereby, an original predictor‐corrector algorithm is developed to effectively coordinate the different numerical algorithms involved, obtaining a robust but efficient yet solution procedure. Various large‐scale ill‐conditioned benchmark systems are studied under different stressing conditions. The results obtained with the developed technique are promising, outperforming other robust and standard PF solvers. [ABSTRACT FROM AUTHOR]

With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff. [ABSTRACT FROM AUTHOR]

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies. • A novel methodology for optimal planning photovoltaic-battery systems. • Grid outages are characterized via scenarios generated from historical data. • The response to demand incorporates different price-based incentives. • Optimal programming of typical deferred devices. • Several case study on a smart prosumer environment are analysed. [ABSTRACT FROM AUTHOR]

• Local markets with eventual local electricity prices might be beneficial for community users. • Local electricity tariffs should be stable and send coherent response signals easily interpretable by users. • Necessity of developing proper designing tools for local electricity tariffs is clear. • The designer is able to tune up the total number of pricing signals. • A designed bi-level Stackelberg-based problem. In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users' preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points. • A new game-based pricing mechanism for parking clusters is developed. • Two uncertainty-aware strategies are proposed and compared. • Vehicle-to-grid helps to improve the economy of the cluster notably. • The impact of uncertainties strongly depends on vehicle-to-grid availability. • The new proposal reduces the cost of users compared with other approaches. [ABSTRACT FROM AUTHOR]

The voltage stability analysis can be carried out using different continuation techniques. The continuation power flow that evaluates all power-flow solutions at different loading levels, is typically considered the most standard one. This approach is mainly based on two stages called predictor and corrector. The standard Newton–Raphson method is usually used at the corrector stage. In this paper, several corrector techniques based on efficient Newton-like methods are proposed in order to speed up the solution of continuation power flow. Consequently, two high order Newton-like methods as well as a fast corrector technique based on the Dishonest NR method have been proposed. A comprehensive study is addressed in order to check the suitability of the proposed corrector techniques compared with the standard Newton–Raphson (NR). Several small, medium, large and very large-scale test systems are used to achieve the validation. The obtained results prove the effectiveness and superiority of proposed techniques compared with the standard NR which conventionally used in corrector stage of the continuation power flow analysis in term of computational time. [ABSTRACT FROM AUTHOR]

Solving power-flow (PF) problem of ill-conditioned systems is still a challenge in realistic power systems due to most of available techniques are quite inefficient. This study aims to address this issue by introducing a novel PF solution paradigm. It basically reformulates the traditional continuous Newton's method in a multistep scheme, so that, the variables are progressively refined each step using different numerical arrangements. The developed solution paradigm envisages a novel family of PF techniques. For the sake of exemplify, two novel methods based on the introduced solution paradigm are developed. They are tested in several large-scale realistic ill-conditioned systems under different stressing and demanding conditions. Up to eight well known PF solution techniques are considered for comparison. Results shown that the introduced solution paradigm constitutes a promising framework for developing robust and efficient PF methods, which may be widespread used in industry applications. [ABSTRACT FROM AUTHOR]

Solving power‐flow (PF) problem of ill‐conditioned systems is still a challenge in realistic power systems due to most of available techniques are quite inefficient. This study aims to address this issue by introducing a novel PF solution paradigm. It basically reformulates the traditional continuous Newton's method in a multistep scheme, so that, the variables are progressively refined each step using different numerical arrangements. The developed solution paradigm envisages a novel family of PF techniques. For the sake of exemplify, two novel methods based on the introduced solution paradigm are developed. They are tested in several large‐scale realistic ill‐conditioned systems under different stressing and demanding conditions. Up to eight well known PF solution techniques are considered for comparison. Results shown that the introduced solution paradigm constitutes a promising framework for developing robust and efficient PF methods, which may be widespread used in industry applications. [ABSTRACT FROM AUTHOR]

Decarbonization of the energy sector calls for new energy sources and power-system operation paradigms. In this context, consumers can partake in system operation as loads and generators by leveraging own resources, such as rooftop photovoltaics (PVs). These prosumers can be gathered into energy communities (ECs) and operated in a centralized manner to exploit eventual synergies optimally. The ECs are usually connected to a distribution network and thus interact with the distribution grid operator (DSO), complicating the scheduling strategies under agent interactions. Moreover, the DSO can thus manage his own resources such as distributed generators or medium-scale storage systems. The operation becomes more complex due to multi-source uncertainties in ECs and DSO. This paper addresses this issue by developing a novel bi-level energy management framework for the robust optimal coordination of ECs and DSO against uncertainties. In this framework, the upper-level agents provide the information by solving the designed optimization problems for the EC operation, sending it to the lower-level agents to facilitate the optimal scheduling of distributed assets. In particular, a novel robust reformulation of the lower level problem is proposed, to handle with uncertainties in renewable generation, demands, and energy prices fixed by the local retailer. The resulting formulation is mixed-integer-linear programming that is efficiently manageable by common solvers. Simulation results on the standard IEEE 33-bus distribution system with six connected ECs and various distributed assets (e.g., renewable generators and storage systems) demonstrate the effectiveness and robustness of the proposed framework. The results also evidence the vital role of storage systems in maximizing the DSO's profits while reducing the ECs' operating costs. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on solving the power‐flow (PF) problems of badly initialised/ill‐conditioned power systems, where the solution of these systems is a challenge for most of state‐of‐the‐art PF techniques. This challenge is increased in case of large and very large‐scale power systems which have a huge number of variables. Consequently, the PF techniques used should have a high degree of efficiency in order to handle the large vectors and matrix computations. In this study, a novel PF approach based on the Richardson extrapolation is proposed to solve these systems. The proposed approach is validated using various badly initialised/ill‐conditioned systems, comparing its performance with several well known PF techniques. The obtained results prove that the proposed approach is robust and efficient enough to properly manage the badly initialised/ill‐conditioned systems even if they consist of a huge number of buses, whereas the other PF techniques face different convergence difficulties. [ABSTRACT FROM AUTHOR]

Load flow (LF) is an extensively used tool in planning and operation of power systems. Formulation of LF problem can be assimilated as a set of autonomous ordinary differential equations, therefore, many numeric methods can be used to solve this problem. However, LF methods often need to compute one or more Jacobian matrix inversions in each iteration. Owing to this fact, these methods might not be computationally efficient. In this study, the authors propose combined Runge-Kutta Broyden's LF (RK4B) method in order to reduce the required Jacobian matrix inversion to only one in the first iteration. In this proposed method, Broyden's approach is employed in fourth-order Runge-Kutta method. In addition, two modifications of the proposed method are presented to reduce the number of iterations and improve the computational performance. The proposed method and the two modifications are validated using several well- and ill-conditioned cases. Results show that the combined approach has better computational performance than the classical multistage numeric methods, besides it preserves the robustness features of fourth-order Runge-Kutta method. [ABSTRACT FROM AUTHOR]

Load flow (LF) is an extensively used tool in planning and operation of power systems. Formulation of LF problem can be assimilated as a set of autonomous ordinary differential equations, therefore, many numeric methods can be used to solve this problem. However, LF methods often need to compute one or more Jacobian matrix inversions in each iteration. Owing to this fact, these methods might not be computationally efficient. In this study, the authors propose combined Runge–Kutta Broyden's LF (RK4B) method in order to reduce the required Jacobian matrix inversion to only one in the first iteration. In this proposed method, Broyden's approach is employed in fourth‐order Runge–Kutta method. In addition, two modifications of the proposed method are presented to reduce the number of iterations and improve the computational performance. The proposed method and the two modifications are validated using several well‐ and ill‐conditioned cases. Results show that the combined approach has better computational performance than the classical multistage numeric methods, besides it preserves the robustness features of fourth‐order Runge–Kutta method. [ABSTRACT FROM AUTHOR]

• A robust energy management tool for isolated microgrids is developed. • Hydrogen chain and various types of flexible loads are modelled. • The optimization framework is solved through a master-slave scheme. • Hydrogen storage becomes useless under robust conditions. • Flexible demand provides higher economic benefits than hydrogen storage. Hydrogen is called to be one of the main energy vectors in future energy systems, especially for energy storage, where this carrier presents some interesting features. Its application in microgrids might help to improve economic, environmental and reliability indicators, providing greater storage capacity than other technologies, like batteries. Following this idea, this paper develops a robust energy management methodology for isolated microgrids considering hydrogen storage and demand response initiatives. The new proposal is raised as a nested max-min optimization framework. To reduce the original problem to a tractable single-level one, a master-slave scheme is developed by which binary variables can be fixed and thus the inner problem can be reduced to its equivalent Karush-Kuhn-Tucker conditions. Then, the overall optimization paradigm is solved in an iterative fashion used the Constraint-and-Column Generation Algorithm. The resulting tool is applied to a benchmark isolated microgrid, thus validating it for industry applications. Moreover, different relevant results are analysed in-depth. Thus, the role of hydrogen storage and demand response initiatives is discussed, concluding that flexible demand has a more notable impact on monetary savings than hydrogen storage, reducing the total cost by 6 % with respect to the base case. Some intrinsic issues are also identified. For example, it is observed that flexible consumers are more frequently called when the hydrogen chain is enabled, which may provoke undesirable effects like response fatigue. [ABSTRACT FROM AUTHOR]

Extreme events such as floods, hurricanes, earthquakes, and wildfires pose significant threats to the uninterrupted supply of electricity to consumers, as they can cause the failure of numerous power system components. The resilience of a power system is defined by its ability to withstand extreme events and continue meeting demand. While the placement of multi-carrier microgrids (MGs) in power systems can impact resilience, the literature lacks research on resilience-oriented placement. This paper aims to address this gap by proposing a resilience-oriented placement strategy for multi-carrier MGs, utilizing switchable transmission lines to enhance the resilience of power systems. The study focuses on a heat-power-hydrogen MG that obtains gas from a gas network, exchanges power with the main power system, and incorporates combined heat and power (CHP), electrolyzer, and thermal storage. The developed model employs a stochastic mixed-integer linear programming (MILP) approach, ensuring the attainment of the global optimum. Resilience is evaluated using the metric of expected load not supplied (ELNS). The results demonstrate that when the bus connected to the MG is isolated, the MG generates electricity through its CHP unit to meet local demand, reducing the total demand shed in the power system and improving system resilience. Specifically, the MG reduces ELNS from 4955.48 MWh to 2356.64 MWh, indicating a remarkable 52% improvement in ELNS. Furthermore, the study shows that transmission line switching further decreases ELNS from 2356.64 MWh to 848.68 MWh. Several experiments are conducted to analyze the sensitivity of power system resilience to the number of MGs, the MG-power system exchange limit, and the limit on gas import from the gas network. • Heat-power-hydrogen microgrids (MG's) are used to improve power system resilience. • Resilience-oriented optimal placement of MGs has been done. • Transmission line switching is used to improve the resilience of power systems. • The effect of the number of MGs on power system resilience is assessed. • Sensitivity of system resilience to MG-power system exchange limit is assessed. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has severely increased the concerns over the resilience of power systems. Extreme events may lead to the failure of a large number of system components and result in interruption of supply to lots of consumers. In this research, the main objective is to use and assess the potential of large industrial energy hubs (EHs) in resilience improvement of power systems. Mobile energy storage and demand response programs are also used to decrease involuntary demand shed in system and enhance system resilience. A stochastic mixed-integer linear programming model is developed considering the uncertainties in damaged transmission lines, hurricane time and repair time. Case study is a modified IEEE 24-bus power system with EHs. The studied EH receives gas from a gas network, exchanges power with power system, includes combined heat and power (CHP), generator, electrolyzer, boiler, thermal storage and hydrogen storage system and feeds electric, heat and hydrogen demands. Expected load not supplied (ELNS) is used as resilience metric. The results approve significant impact of EH in improvement of power system resilience. In scenarios in which the bus connected to EH is isolated, EH supplies the power system demand located at that bus and thereby improves system resilience; on the other hand, in other scenarios, EH typically behaves as an electricity consumer of power system. According to the results, mobile energy storage causes a 2.4% improvement in ELNS. The results also show that demand response program improves ELNS by 7.1%. • Energy hub and mobile storage are used for resilience enhancement of power systems. • Uncertainties in damaged lines, hurricane time and repair time are considered. • The effect of responsive demands on power system resilience is evaluated. • The effect of EH generator size on power system resilience is assessed. [ABSTRACT FROM AUTHOR]

In this research, a computationally-inexpensive stochastic MILP model is proposed for the optimal placement of hydrogen fuel stations (HFSs) in power systems with high penetration of renewables, while power system operator sells its extra hydrogen in a day-ahead local hydrogen market. In the developed model, a linearisation strategy is proposed to transform the nonlinear binary terms into linear terms and the uncertainties of electricity and hydrogen demands, photovoltaic (PV) generation and hydrogen market prices are modeled as scenarios. Any available HFS includes an electrolyzer and a hydrogen storage system. As the penetration of renewables in the studied power system is high, most of the produced hydrogen is green. According to the results, system operator uses the potential of hydrogen storage systems and responsive electricity demands to sell more hydrogen to the local hydrogen market and increase its profit. In times with higher hydrogen prices, system operator commands both shift-down in electricity demands and discharge mode for batteries to be able to sell more electricity and make more profit; on the other hand, in times with lower hydrogen prices, system operator commands shift-up in electricity demands and charge mode for batteries. The results show that demand response program increases expected profit of system by 4.7%. The results confirm that addition of HFSs strongly decreases PV curtailment. The impact of the participation in hydrogen market on system profit is assessed. The sensitivity of HFS profit to the number of HFSs, size of electrolyzers and demand response participation factor is assessed. • Optimal location of hydrogen fuel stations (HFSs) is found in power systems. • A linearisation strategy is used to transform bi-linear terms into linear terms. • Uncertainties of demands, PV generation and hydrogen market price are considered. • The sensitivity of the power system profit to model parameters is assessed. • Impact of the participation in hydrogen market on power system profit is assessed. [ABSTRACT FROM AUTHOR]

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed. [Display omitted] • A model is developed for electricity procurement of hydrogen fuel stations (HFSs). • The uncertainties in demands and market prices are considered. • HFS procures electricity by day-ahead and balancing market and bilateral contracts. • Impact of different markets and transactions on HFS profit is assessed. • Sensitivity of HFS profit to weight of risk metric is assessed. [ABSTRACT FROM AUTHOR]

• A new price setting mechanism for microgrid clusters is developed. • The new approach only requires sharing boundary information. • Uncertainties are incorporated using interval notation and equivalent scenarios. • The novel proposal encourages the participation in energy trading. • The strategy adopted by agents notably impact on the energy transacted. With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids. [ABSTRACT FROM AUTHOR]

• An optimal bidding strategy for microgrids in clusters is developed. • A multi-stage framework is proposed for considering response fatigue. • Uncertainty risk is managed by enforcing the CVaR. • The total number of response signals can be reduced by 76 %. • The response fatigue minization may reduce the collective welfare by 18 %. The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account. [ABSTRACT FROM AUTHOR]

• A novel home energy management tool for optimal integration of prosumers in energy communities is developed. • The new proposal is based on a novel stochastic-robust formulation to account for heterogeneous uncertainties. • Trading with different agents within communities is modelled and analysed. • Storage assets are of vital importance to increment the exportable capacity of the dwelling. • Trading with the community and other peers are normally preferable but highly affected by uncertainties. With the advent of smart grids, novel businesses like energy communities are becoming more frequent, thus enabling alternative energy transactions for smart prosumers like peer-to-peer mechanisms, that may increment the efficiency of residential installations while reducing the electricity bill. However, the optimal participation in such frameworks is a formidable challenge because the multiple uncertainties involved and energy paths enabled, which increments the number of decision variables and pricing mechanisms. This paper addresses this issue by developing a novel day-ahead scheduling model for prosumers integrated in energy communities based on a stochastic-robust approach. The developed formulation contemplates energy transactions with the utility grid, the community and other peers, besides the intrinsic uncertainties that arise from these processes. The heterogeneity of the unknown parameters is effectively addressed by using different uncertainty models, thus, while the predictable parameters are modelled using robust formulation, the highly volatile uncertainties are treated via scenarios. A case study is presented with the aim of validating the new tool as well as analyse the different energy transactions and their monetary implications. The obtained results evidence the important role of storage assets in reducing the electricity bill by 86 %, which is achieved by incrementing the exportable capacity of the dwelling by 84 %. The impact of uncertainties is also studied, expecting more pessimistic profiles at expenses of incrementing the monetary cost in 0.37-$. [ABSTRACT FROM AUTHOR]

The proliferation of green mobility will bring multiple benefits to the society; however, it may be counterproductive for power systems if its integration is not properly planned. In this context, Intelligent Parking Lots have emerged as a valuable paradigm for integration of electric vehicles into energy systems. This framework consists of a set of vehicles that are managed as a whole and makes possible to exploit them as large storage facilities through their vehicle-to-grid capability. This particular feature may be significantly advantageous for energy communities since they can exploit parking lots as collective storage systems. In this paper, a two-stage optimal scheduling framework has been developed for optimal scheduling of energy communities. The proposal uses a stochastic representation of the state-of-charge of the lots with the end of accounting for random behaviour of uncertainties. On the other hand, the uncertainty of the upstream energy market is dealt with Information Gap Decision theory, resulting in an original hybridization that allows to adopt a risk-averse strategy by the operator. The optimization problem is formulated as a Mixed-Integer Linear programming model that can be efficiently solved by average solvers. A case study is performed to validate the new proposal and analyse the role of Intelligent Parking Lots in energy communities. The results evidence the advantages that electric vehicles may bring to communities if they are optimally exploited, highlighting their capability to enhance the efficiency and economy of the system. • A novel scheduling tool for energy communities encompassing IPLs is developed. • A stochastic model is proposed for SOC estimation in IPLs. • Upstream energy pricing is modelled using IGDT. • Results reveal the importance of optimally sizing IPLs. • Selling energy to the grid mainly determine the monetary balance of the community. [ABSTRACT FROM AUTHOR]

• A novel hybrid stochastic-IGDT model is developed for HEM problems in nZEBs. • Real energy price and vehicle-to-home capability are properly modelled. • Unpredictable behavior of the electric vehicle is formulated via IGDT. • A bi-objective structure is presented to jointly optimize the energy consumption and cost using lexicographic optimization. • The problem is kept linear being solvable by standard solvers. Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet. [ABSTRACT FROM AUTHOR]

• A novel stochastic-robust approach is developed for energy communities. • Peer-to-peer exchanges and collective assets are considered and analysed. • The problem is solved using a novel three-stage procedure. • Operating cost is incremented 77$ when the degree of robustness grows. • Collective storage systems help to reduce unserved demand and wasted generation. Energy communities enable effective coordination among prosumers on pursuing collective targets. This paper focuses on isolated 100% renewable communities, involving individual (controllable appliances and small generators) and collective (wind generators and battery banks) assets. To effectively coordinate the agents involved in these structures, advanced energy management strategies are necessary. This work develops a three-stage day-ahead scheduling strategy for isolated 100% energy communities, involving peer-to-peer transactions among prosumers. The different uncertainties involved are incorporated through a novel stochastic-robust formulation, that results in a computationally tractable optimization framework. To validate the new model, a case study on a six-prosumer benchmark community is analysed. Results reveal the importance of collective assets and peer-to-peer exchanges among prosumers as well as the effectiveness of the developed formulation. The role of batteries is also discussed, helping to reduce the total unserved energy and operating cost by 20% and 19%, respectively, as well as enabling a more efficient use of wind energy. The impact of robustness is also studied, incrementing the expected importable energy by 28% compared to the deterministic case, while the exportable energy from prosumers is notably reduced by 40%. However, uncertainty-aware strategies have a direct impact on operational costs, incrementing the expenditures by 37% when uncertainties are considered. [ABSTRACT FROM AUTHOR]

In order to mitigate greenhouse gas emissions and improve energy efficiency, sustainable energy systems such as multi-energy microgrids (MEMGs) with the high penetration of renewable energy resources (RES) and satisfying different energy needs of consumers have received significant attention in recent years. MEMGs, by relying on renewable resources and energy storage systems along with energy conversion systems, play an essential role in sustainability of energy supply. However, renewable energies are uncertain due to the intermittent nature of solar and wind energy sources. Thus, optimal operation of the MEMGs with the consideration of the uncertainties of RES is necessary to achieve sustainability. In this paper, risk constrained scheduling of a MEMG is carried out with the presence of the PV, wind, biomass, electric vehicles (EVs) and hydrogen vehicles (HVs) charging stations, combined heat and power (CHP), boiler, hydrogen electrolyzer (HE), cryptocurrency miners (CMs), electrical, thermal and hydrogen storage systems, responsive demands. From the trading and business model side, the proposed MEMG optimized operation relies on bilateral contracts between producers and consumers and pool electricity markets. A two-stage stochastic programming method is used for considering the uncertainties of electrical, thermal and hydrogen demands, EV and HV charging stations load, CM load, PV and wind power, and the price of electricity purchased from the pool market. The proposed mixed integer linear programming (MILP) model is solved using the CPLEX solver in GAMS which guarantees to achieve a globally optimal solution. The results show that due to the certain prices of bilateral contracts, the possibility of transaction by bilateral contracts decreases the risk metric CVaR by 50.42%. The simulation results demonstrate that risk of high operation costs while considering flexibility sources, such as storages and demand response (DR) programs, is decreased by 5.45% and 4.6%, respectively. As far as operation costs are concerned, results reveal that using renewable resources decreases operation costs by 34.47%. Moreover, the operation cost is reduced by 5.94% and 4.57% in the presence of storage units and DR programs, respectively. In the same way, storages and DR programs decrease cost of purchased electricity by 13.47% and 14.46%, respectively. • Risk-aware scheduling of microgrids (MGs) is done considering the uncertainties. • Besides pool market, bilateral contracts are used as a certain source of electricity. • Effect of different contingencies on the operation of MG is studied. • Effect of different demand response programs on operation of MG is assessed. • Effect of different storage systems on operation cost of MG is discussed. • The effect of confidence level and weight factor on MG operation is investigated. [ABSTRACT FROM AUTHOR]

One of the main barriers for the wide penetration of fuel cell electric vehicles is the lack of proper infrastructures for hydrogen transportation that hinders the implantation of refuelling stations. This barrier could be overcome by deploying onsite hydrogen generators based on mature electrolysis and hydrogen storage technologies. This way, the necessity of hydrogen transportation is avoided. In addition, electrolysers can be onsite supplied by means of renewable generators like photovoltaic panels, while the produced hydrogen can also be destined to generate electricity through fuel cells thus obtaining a monetary revenue. Thereby, the economy of the system may be improved in order to make viable this kind of infrastructures. However, the optimal coordination of the different assets is challenging and requires the use of energy management tools to pursue the optimal performance of the installation. In this kind of infrastructures, the energy management problem is performed under substantial uncertainties; moreover, these unknown parameters have a very different character. Thus, while energy pricing and renewable generation can be forecasted using conventional techniques, refuelling demand is highly unpredictable. To this end, this paper proposes a novel stochastic-interval model for the optimal scheduling of photovoltaic-assisted refuelling stations. The new proposal uses interval notation to model the inherent uncertainty of renewable generation and energy pricing, while the vehicle demand is modelled using a more suitable approach based on scenarios. In this regard, a comprehensive stochastic model for fuel cell electric vehicles is developed, which is based on reported driving behaviour and common characteristics of commercial vehicles. To solve the problem subjected to uncertainties, an iterative solution methodology is developed which allows adopting risk-seeker and risk-averse operational strategies. A case study is analysed to validate the new proposal and discussing the importance of the different economic activities that can be exploited in refuelling stations. Results reveal the importance of selling energy to the grid in order to complement the revenues obtained from refuelling; however, this process is highly impacted by uncertainties and the operational strategy, observing variations up to 50% in the total profit depending on the strategy adopted. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted. • Home Energy Management Systems have gained importance to help home users. • A novel hybrid robust-multi objective home energy management approach. • The novel proposal is based on information gap decision theory and lexicographic optimization. • The developed mathematical formulation is Mixed Integer Linear Programming. • The developed methodology is able to deal with uncertainties. [ABSTRACT FROM AUTHOR]

• Developing a novel Stochastic-IGDT approach for optimal scheduling of isolated microgrids. • Considering failure of components in the developed model via IGDT. • Considering other uncertainties using scenarios. • Developing a solution methodology for the considered optimal scheduling model. • Performing simulations to validate the new proposal and analyse the role of demand response programs. In power systems, contingencies and outages are more frequent nowadays due to climate changing effects. This circumstance along equipment aging may lead to unexpected failures and outages in power and energy systems. This issue is especially critical in isolated microgrids, which must be supplied by means of own resources and onsite assets. In these systems, unexpected failures may notably provoke a detriment of the economy and users' satisfaction. In order to minimize the impact of these incidents, this paper proposes a novel energy management tool for isolated microgrids that are robust against failures. To this end, a novel stochastic-IGDT formulation is developed, by which typical uncertainties are comfortably modelled via scenarios while components' failures are treated in a robust fashion using IGDT. A solution procedure is proposed in which the operation cost is also considered in order to reproduce useful results and limit the cost of reliability. A variety of simulations are conducted in order to validate the developed model and discuss the particularities derived from considering failures in the energy management task. Moreover, the role of demand response programs is also foregrounded. In particular, the demand response programs allow reducing the operation costs by 3 % while the scheduling result admits up to 13 h more of accumulated failures, thus confirming a positive effect of such initiatives in both economy and robustness against failures. [ABSTRACT FROM AUTHOR]

• A novel uncertainty-aware day-ahead scheduling tool for microgrids considering response fatigue is developed. • The novel methodology presents a modular and linear formulation, being thereby solvable by conventional solvers. • Various types of demand response programs are modelled and analysed. • Uncertainties and response fatigue are jointly treated by an original information gap decision theory-based methodology. • A benchmark case study serves to show the capabilities and effectiveness of the developed method. The implantation of demand response programs may be unsuccessful due to a variety of reasons. One of the most important is the so-called response fatigue, which refers to the discouragement experienced by consumers when they received an excessive number of signals from the operator. This circumstance is, however, typically ignored in energy management tools of electrical energy systems. To solve this issue, this paper proposes an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids based on information gap decision theory, which incorporates additional constraints to bound the duration of demand response signals. Thereby, the harmful effects caused by response fatigue are lessened. The developed optimization problem is formulated as a Mixed-Integer-Linear programming, which is solvable using standard solvers and versatile enough to be adapted to different system layouts. A benchmark case study serves to show the effectiveness of the developed methodology to manage with uncertainties, while the effect of response fatigue in consumers is bounded to acceptable thresholds. As a sake of example, the developed methodology is able to determine a scheduling plan with a total renewable generation 50% lower compared with the deterministic case, while the total demand is overestimated by ∼20%, in which the effect of response fatigue is kept within acceptable bounds yet. Accurateness and efficiency of the new proposal are also checked by making a comparison with other uncertainties modelling. [ABSTRACT FROM AUTHOR]

• A Many-objective optimization framework for Home Energy Management tools. • Lexicographic optimization and scalarizing functions. • The developed tool allows to distinguish one of the targets as the most important one. • Stochastic and Mixed Integer Linear Programming frameworks. • A benchmark case study serves to validate and assesses the computational efficiency. Home Energy Management systems have become an essential tool on pursuing an efficient energy consumption besides the implantation of demand response programs in residential consumers. This kind of systems have evolved from a single-objective paradigm, in which only the electricity bill was concerned, to many-objective frameworks that involve different targets such as grid supporting programs or user convenience premises. From the computational point of view, many-objective optimization problems are more challenging than single-, and multiple-objective ones due to higher computational burden and the necessity of advanced tools capable to reach a compromise solution among objectives. This paper tackles this issue by developing an efficient but effective stochastic many-objective solution framework based on Mixed Integer Linear Programming formulation. The new proposal is based on lexicographic optimization and scalarizing functions, thus allowing to distinguish one of the targets as the most important one. This feature makes the new approach very suitable for Home Energy Management systems in which the energy cost is normally the most important objective while other goals can be considered secondary. The developed tool is validated on a standard home layout benchmark considering up to six typical objectives. The results obtained put on manifest the effectiveness and efficiency of the developed approach. [ABSTRACT FROM AUTHOR]

In recent years, multi-energy microgrids (MEMGs) have emerged as an invaluable framework for enabling the use of clean and efficient electro-thermal resources as well as the integration of multi-energy storage facilities. Uncertainties modelling in such systems is a challenge because of the heterogeneity of the resources and consumers involved. This paper tackles this issue by proposing a hybrid robust energy management tool for MEMGs encompassing electric, heat, hydrogen and gas sub-networks. The variety of uncertainties brought by unpredictable demand and renewable generation are managed using adequate techniques. This way, renewable generation is modelled using the Hong 2m + 1 approach, the electrical and heat demands are managed using the information gap decision theory and the fuel-cell electric vehicles refueling demand is modelled via scenarios. The novel methodology is validated on a benchmark case study, in which extensive simulations are performed. The obtained results demonstrate the accurateness of the novel proposal and its effectiveness to manage a wide variety of uncertainties. The evidence for accurateness is that the difference in the objective function with the Monte Carlo and Hong 2m + 1 uncertainty modelling approaches only differs by ∼0.2%. Moreover, the new proposal is computationally competitive with the Monte Carlo simulation, improving its computation time by 2–3 times. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel MILP framework is developed for tariff-choosing decision process. • The developed is applicable to the so-called happy hours tariffs. • The new proposal offers the most suitable hours to be hired as happy hours. • Various useful results can be directly obtained from the developed framework. • A case study on the Spanish retail market on a benchmark smart prosumer. Nowadays, electricity end users can choose among a huge variety of different electricity plans on a deregulated energy market. The wide variety of tariffs besides the advent of novel agents like smart consumers and prosumers, are becoming the tariff-choosing process more complex. This paper proposes a MILP optimization framework which aims at facilitating this task. More precisely, the main endings of the developed framework are: (i) determine the most suitable tariff for smart consumers and prosumers based on historical consumption data, (ii) determine the optimal hours to be hired for a so-called 'Happy hours' tariff plan. In addition, other useful results can be directly obtained from the developed tool. The developed approach carries out a MILP optimization framework for optimal scheduling a series of flexible appliances through various characteristic days obtained from clustering historical collected data. This process is repeatedly executed for the different tariff options and, finally, the most attractive one is selected. A case study on the Spanish retail market for a benchmark prosumer environment is used for showing the capabilities of the developed framework. [ABSTRACT FROM AUTHOR]

• A MILP-based formulation for optimal planning of electrification systems. • Different demand-side strategies can be incorporated. • A case study with different layouts and demand-side management strategies. • The cost can be reduced up to 41.5% The emergence of novel demand-side management strategies is provoking that residential consumers cannot be longer categorized as inflexible loads. In that sense, new paradigms such as flexible demand and vehicle-to-home capabilities have appeared on pursuing a more efficient management of the different domestic assets such as smart appliances, small renewable generators and storage facilities. Consideration of such kind of management strategies plays a vital role on designing stages of electrifications systems for isolated homes. In this regard, a proper evaluation of possible demand-side capabilities enables a more accurate evaluation of the electrification project. This paper tackles this issue by developing a Mixed Integer Linear Programming formulation for optimal planning of electrification systems for off-grid dwellings. The developed framework allows to incorporate and analyse advanced demand-side strategies such as deferrable loads and vehicle-to-home processes by evaluating different project costs over various time horizons. The proposed approach is applied to a benchmark off-grid residential case study and various results are provided and analysed. As the most relevant result, it is worth remarking that total project cost can be reduced by ~21.2% and ~25.73% by considering flexible demand and vehicle-to-home capabilities, respectively. Nevertheless, the highest monetary savings were achieved when both strategies are jointly adopted, reducing the total project cost up to ~41.5%. The reported results demonstrate the importance of considering the different demand-side strategies on planning stages of electrification systems for off-grid dwellings. [ABSTRACT FROM AUTHOR]

Recently, the high-order Newton-like methods have gained popularity for solving power flow problems due to their simplicity, versatility and, in some cases, efficiency. In this context, recent research studied the applicability of the 4th order Jarrat's method as applied to power flow calculation (PFC). Despite the 4th order of convergence of this technique, it is not competitive with the conventional solvers due to its very high computational cost. This paper addresses this issue by proposing two efficient modifications of the 4th order Jarrat's method, which present the fourth and sixth order of convergence. In addition, continuous versions of the new proposals and the 4th order Jarrat's method extend their applicability to ill-conditioned cases. Extensive results in multiple realistic power networks serve to sow the performance of the developed solvers. Results obtained in both well and ill-conditioned cases are promising. [ABSTRACT FROM AUTHOR]

In off-grid dwellings, proper energy management of different appliances, onsite generators and storage facilities is crucial to meet some monetary goals and environmental premises such as CO 2 emissions and diesel consumption reduction. Nowadays, this task may be performed by home energy management systems, which are able to efficiently coordinate the operation of the different home assets to achieve some predefined goals. In such cases, the operation of thermostatically controlled appliances such as heating-ventilation-air conditioning systems, electric water heaters and freezers, plays a vital role as this kind of devices allow certain degree of flexibility. This way, some thermal premises (such as thermal comfort, frozen food conservation or hot water temperature set-point) can be relaxed in order to achieve other complementary goals. This work analyses this aspect by proposing a home energy management model which incorporates flexible operational modes of various typical thermostatically controlled devices. Various results are presented on a benchmark isolated home which counts with a diesel engine as backup generator. Results show that energy generated by the backup generator can be reduced by 15% by operating the thermal-based appliances in a flexible way, while other relevant indicators such as fuel consumption, fuel cost and CO 2 emissions can be also improved by 12%. Other relevant aspects such as the impact of the photovoltaic array size and storage capacity are also investigated. [ABSTRACT FROM AUTHOR]

This work presents a comprehensive analysis of two cubic techniques for Power Flow (PF) studies. In this regard, the families of Weerakoon-like and Darvishi-like techniques are considered. Several theoretical findings are presented and posteriorly confirmed by multiple numerical results. Based on the obtained results, the Weerakoon's technique is considered more reliable than the Newton-Raphson and Darvishi's methods. As counterpart, it presents a high computational burden. Regarding this point, the Darvishi's technique has turned out to be quite efficient and fully competitive with the Newton's scheme. [ABSTRACT FROM AUTHOR]

In order to mitigate the influence of global warming and greenhouse gasses emission, different proceedings are suggested such as utilizing renewable energies and demand response programs. This paper investigates the expansion of renewable resources and electrical energy storage units in distribution systems towards reducing investment costs and environmental pollution. Since the development of components is not possible in a single-stage due to the limitation of staff and funds, a multi-stage programming is applied to consider various restrictions. According to the penetration of crypto-currency miners in recent years, their impact is evaluated on the problem as the high-rate energy consumers. Moreover, the demand side management strategy and risk-averse scenario-based approach are implemented to analyze the role of responsive loads and model the renewable resources uncertainty, respectively. The results approve that simultaneous expansion of wind turbines, photovoltaics and electrical storage systems decreases the total pollution and cost by 100% and 99.98% after the third year, respectively. The simulations also validate that crypto-currency miners reduce the total revenue by 4.16%, whereas the responsive loads increase it by 9.89%. As well, the fluctuations of wind and solar power decrease the total revenue by 13.27%, in return, the robustness notably improves. [ABSTRACT FROM AUTHOR]