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Solar photovoltaic (PV) systems are becoming an indispensable source of energy for residential customers and are contributing to increase the sustainability of energy systems. However, practical methods that can aid customers to make informed decision on installing the PV systems considering their integration with the residential energy system have remained scarce. This paper proposes a probabilistic dynamic planning model for optimizing decisions on installing PV systems for a residential energy hub. In addition to the PV system, residential energy hub integrates combined heat and power (CHP) units and gas boilers to supply the heat and electrical demands. The proposed planning model minimizes the investment cost of PV systems along with the energy cost of energy hub that includes possible PV energy curtailment cost over the planning horizon. The proposed problem also integrates a heat storage system and responsiveness of the heat and electrical demands to the respective gas and electricity prices as additional flexibility to increase the benefits of PV systems for the customers. Two-point estimate method is used to model the uncertainty of PV generation. The proposed method is implemented on a test energy hub for a 10-year planning period to demonstrate features of the proposed method. [ABSTRACT FROM AUTHOR]

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day-ahead scheduling for multi-carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi-carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi-period optimal energy flow problem in multi-carrier energy networks, this study utilises the well-known teaching-learning based optimisation algorithm. The investigated multi-carrier energy system consists of electrical, natural gas and district heating sub-networks in which an ESS is included in the electrical sub-network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively. [ABSTRACT FROM AUTHOR]

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day‐ahead scheduling for multi‐carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi‐carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi‐period optimal energy flow problem in multi‐carrier energy networks, this study utilises the well‐known teaching‐learning based optimisation algorithm. The investigated multi‐carrier energy system consists of electrical, natural gas and district heating sub‐networks in which an ESS is included in the electrical sub‐network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively. [ABSTRACT FROM AUTHOR]

Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programing approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors, and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short-time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer nonlinear programing optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network. [ABSTRACT FROM AUTHOR]

This paper investigates energy flow of Multi-Carrier Energy Systems using Point Estimate Method and Monte Carlo simulation considering uncertainties, which may happen in the Electrical, Natural Gas, and District Heating Networks all together. These uncertainties could occur from the probabilistic behavior of loads or unforeseen faults. An innovative probabilistic energy flow of Multi-Carrier Energy Systems based on Point Estimate Method has been presented in this paper. Results for two different case studies are investigated and compared against those achieved from the Monte Carlo simulation. The results prove that the presented point estimate schemes have precise results, smaller computational burden, and time, comparing than Monte Carlo simulation method. [ABSTRACT FROM AUTHOR]

Expansion of cogeneration technologies, such as combined heat and power units, has boosted the growth of multi-carrier energy systems. A two-step method is presented to enable residential demand response (DR) programmes in the multi- carrier energy systems. In the first step, the energy management system at each home solves an optimisation problem to achieve the desired energy cost and demand schedule for the customer according to received price signals. In the second step, the system operator revises the demand scheduling by running another optimisation problem to minimise the total electrical losses, subject to the operational characteristics of electrical and natural gas systems. In order to persuade customers to participate in the DR programmes, it is guaranteed that the resulted cost of the second step is not more than the desired cost of the customer in the first step. Results of applying the proposed method, incorporating different penetration levels of customer participation in a time-of-use programme is studied in a test energy system. Simulation results verify the effectiveness of proposed method in minimising the total electrical losses, improving the operational characteristics of the energy system as well as providing customers' utilities. [ABSTRACT FROM AUTHOR]

A multi-carrier energy network is a system consists of several energy carriers such as electricity, natural gas, heat, etc . Generally, each transmission network is optimized separately but it could overshadow the actual optimal operation of the whole energy network. An integrated viewpoint opens a new window on optimization analysis. Among various networks, district heating networks are very promising for energy saving and carbon emission reduction. In this paper, effects of utilizing this network on optimization of a multi-carrier energy system are shown. However, such a problem has a considerable number of variables that makes a non-linear, non-convex, non-smooth, and high-dimension optimization problem and the optimal solution cannot be achieved by conventional mathematical techniques. Therefore, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching-learning based optimization algorithm is used to solve the problem. Various simulations on a typical system show the impacts of a district heating network in the multi-carrier energy system. [ABSTRACT FROM AUTHOR]

In this paper, a generalized heuristic approach is proposed to solve the optimal power flow problem in multicarrier energy systems. This technique omits the use of any extra variable, such as dispatch factors or dummy variables required for conventional techniques. The unified proposed approach can be utilized with all evolutionary algorithms. Modeling hub devices with constant efficiency may produce a considerable error in finding the actual optimal operating point of the whole network. However, using variable efficiency model adds complexity to the conventional methods while increasing the computation–demand of these techniques, but this target can be simply implemented by the proposed scheme. A multicarrier energy system consists of an electrical, a natural gas, and a district heating network is analyzed by the proposed algorithm using the modified teaching–learning-based optimization method. Results validate the utilized approach and show that it can successfully reach the global optimal solution of the problem. [ABSTRACT FROM PUBLISHER]

In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective optimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natural gas, and heat. Minimizing the total cost of operation of such a system is a typical objective for optimization while another important objective is to minimize the total emission generated by the whole network. It is shown in the paper that the cost and emission functions are two opposite objectives that decreasing one of them would increase the other one and vice versa. Therefore, a multi-objective optimization should be utilized to obtain the global optima of the problem based on the priority of each objective. According to the large size of the problem in actual networks, this could be a non-linear, non-convex, non-smooth, and high-dimension optimization problem that mathematical techniques could be trapped in local minima. Hence, it is better to use evolutionary techniques instead. To do so, a fuzzy decision making method is proposed in this paper which is merged with the well-known modified teaching-learning based optimization algorithm. This approach is implemented and applied to a typical multi-carrier energy network to verify the proposed methodology. [ABSTRACT FROM AUTHOR]

• Operation of integrated energy systems in distribution level is modeled. • Constraints of electrical, natural gas, and heating networks are considered. • The electrical network is modeled as an unbalanced three-phase distribution system. • Optimal energy flow of integrated energy distribution system is formulated. • A new method is applied to solve the load flow of unbalanced electrical network. This paper focuses on the operation optimization of the integrated energy distribution systems, including electrical, natural gas, and district heating distribution systems. According to the unbalanced nature of the electrical distribution networks, a comprehensive model of the unbalanced electrical distribution network is established. A non-iterative three-phase load flow method, based on holomorphic-embedding, is proposed and implemented for solving the load flow of the unbalanced radial distribution electrical network. The energy flow of natural gas and heat networks are modeled and solved by a graph theory-based method. Teaching-learning based optimization algorithm is implemented to solve the optimization problem, considering various constraints of the proposed integrated energy distribution system. The objective is minimizing the total operation cost of energy flow in multi-carrier energy networks. The performance of the proposed method is validated in an integrated energy distribution system, including a 19-bus unbalanced radial electrical network, a typical 20-node natural gas, and a 30-node district heating network. The results verify the effectiveness of the proposed integrated energy system operation and applicability of the proposed optimal energy flow. [ABSTRACT FROM AUTHOR]