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In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively. A risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them.

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

Summary In this paper, an algorithm for scheduling of demand‐side resources is presented for residential loads in dynamic pricing environment. The main objective of this study is to minimize the operational cost of energy consuming devices in an entire building over a day without violating set of consumer comfort preferences. The residential end‐use consumer loads considered in the proposed work are heat ventilation and air conditioning system, plug‐in hybrid electric vehicle, electric water pump, and electric water heater. The optimal control operation of the loads under a real‐time pricing scheme is analyzed using particle swarm optimization. The results show that the proposed scheme gives a significant reduction in the building energy cost as compared to the normal ON/OFF based control operation. Two case studies of 2 typical buildings consisting of 3 and 100 houses are taken to evaluate the proposed optimal control scheme. The comparison between the proposed and normal ON/OFF methods shows that global optimization gives the significant energy as well as cost saving.

The design and development of renewable energy resources‐based poly‐generation microgrids have recently increased to supply multiple demands such as cool, heat, and power as well as mitigate pollutants improving efficiency. This paper aims to develop a combined cooling, heating, and power production network integrating photovoltaic panels (PVs), wind and gas turbines, a battery, an ice bank tank, a heater, an electrical chiller, a thermal energy storage medium. In this tri‐generation facility, natural gas is utilized for district heating and fueling the gas turbine power generation cycle. The local power distribution system in combination with the output powers of PVs, wind and gas turbines is used to directly supply the electrical appliances, ice maker process, and chiller as well as charge the battery storage unit. Moreover, the air/water‐cooled chiller procures the cooling flux for a benchmark microgrid. Its heating energy requirement is also provided by the gas‐fired heater, the flue gases of the gas turbine, and the thermal storage medium. A mixed integer linear programming problem is coded using a generalized algebraic modeling system (GAMS) to minimize daily operating costs and emissions. Simulations are examined and analyzed over a 24‐h study horizon on a sample summer day. Time‐of‐use energy rates and RTPs are considered two strategic demand response schemes to investigate the cost‐effectiveness capability of the gas‐power nexus model. The proposed approach is coded using a GAMS to confirm its effectiveness and cost‐environ benefits in four cases with and without heat/cool/electrical storage units considering time‐of‐use energy rates and real‐time prices. In this paper, an optimal operation of the energy hub system in a microgrid is performed. To strengthen the energy hub, heating and cooling power hub have been used. In addition to dynamic system optimization, a pioneering model has been used as a mixed integer linear programming optimization problem, taking into account the demand response to minimize daily operating costs and air pollution.

This study proposes a single-leader-multi follower game to model a bilevel retail market among an aggregator and multiple microgrids to determine the optimal demand scheduling of the consumer, as well as price-power bidding strategies of microgrids in an interactive scheme. In the lower level, microgrids which include several distributed energy resources and energy storage units, compete with each other and offer the optimal energy-price bids such that their individual profit is maximised, while energy dispatch among their energy resources is also determined. Then, in the upper-level problem, the aggregator leads the competition taking advantages of demand-side management including interruptible and shiftable loads to minimise its energy payment for real-time pricing of generation units. By means of Karush–Kuhn–Tucker optimality condition, the bilevel optimisation of Stackelberg game is reduced to a single-level mixed-integer linear programming problem. Moreover, impact of microgrids’ integration on the retail market clearance mechanism, as well as required incentives for such integration has been discussed in a separate scenario.

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

The smart grid technology permits the revolution of the electrical system from a conventional power grid to an intelligent power network which has led the improvements in electrical system in terms of energy efficiency and sustainable energy integration. This study presents the energy management/coordination scheme for domestic demand using the key strategy of smart grid energy efficiency modelling. The structure consists of combining renewable energy resources, photovoltaic (PV) and wind power generation connected to the utility grid with energy storage system (ESS) in an optimal control manner to coordinate the power flow of a residential home. Based on the demand response schemes in the framework of real-time electricity pricing, this work designs a closed-loop optimal control strategy that is created by the dynamic model of the ESS to compute the system performance index, which is formulated by the cost of the energy flows. A dynamic distributed energy storage strategy (DDESS) is implemented to optimally coordinate the energy system, which reduces the total energy consumption from the main grid of more than 100% of the load demand. The designed model introduces a payback scheme while robustly optimising the energy flows and minimising the utility grid's energy consumption cost.

The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort.

It is expected that electric vehicles (EVs) will soon represent a large share of the demand for electricity. Several research works have extolled the advantages of these devices as flexible demands, not only to charge their batteries when it is cheaper to do so, but also to provide services in the form of vehicle-to-grid (V2G) power injections to the system. These services, however, could reduce the useful life of the battery and thus introduce a cost that needs to be taken into account when scheduling the charging of these vehicles. This study presents a scheduling algorithm for EVs under a real time pricing scheme with uncertainty. The objective function explicitly takes into account the cost of battery degradation not only when used to provide services to the system but also in terms of the EV utilisation for motion. The results show that the scheduling of the V2G services is sensitive to the electricity prices uncertainty and to the degradation costs derived from the energy arbitrage. Also, the optimal energy state of charge of the batteries is highly dependent on whether the cost of battery degradation is taken into account or not.