Abstract
Distribution networks have a prominent role in electricity delivery to individual consumers. Nevertheless, their energy losses are higher than transmission systems, which this issue affects the distribution operational costs. Hence, the minimization of power losses in distribution networks has particular importance for the system operators. Distribution network reconfiguration (DNR) is an effective way to reduce energy losses. However, some research works regarding DNR have not considered load variations in power loss calculations. Load level has an essential role in network losses determination and significantly influences the energy losses amount. On the other hand, considering load variations in DNR increases the computational burden and processing time of the relevant algorithms. Therefore, this paper presents an effective reconfiguration framework for minimization of distribution losses, while the energy demand is changing. The simulation results show the effectiveness of the proposed strategy for optimal reconfiguration of distribution systems in presence of load variations.

Abstract
This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.

Abstract
Consumption patterns of electric power systems are important for distribution companies, because of their significant impact on energy losses amount. Therefore, some incentives are suggested by distribution companies to energy consumers for correcting their consumption manner. For a specific load pattern, distribution system reconfiguration (DSR) is an effective way to mitigate energy losses. Hence, some research works have included load variations in the DSR problem to show the importance of consumption patterns in reconfiguration decisions. However, some of the specialized literature has ignored load changes in their reconfiguration models due to the high computational burden and processing time. On the other hand, the energy losses are calculated inaccurately if the consumption pattern is neglected. Consequently, the main goal of this article is to investigate load pattern impact on switching sequences to find out how much is load profile important for minimization of energy losses in DSR. The evaluations were carried out for three well-known distribution systems using a classic optimization tool, the A Mathematical Programming Language.

Abstract
The decrease of short-circuit current in islanded mode due to the existence of resources with low inertial, topology changing, multi-directional current, and telecommunication are the most critical issues encountered by micro-grid protection. The central adaptive protection schemes are widely used for micro-grids, but due to the lack of reliability of this schemes, they are not perfect methods. In this paper, a hybrid scheme of adaptive and multi-agent protection for micro-grid is discussed, which will be able to provide safety protection at several layers and levels, using the equipment in the micro-grid with distributed generation including renewable and nonrenewable energy resources. The proposed scheme calculates relays pickup current with a formula that uses the superposition theorem. To demonstrate the proposed scheme performance, it is implemented and simulated on a sample micro-grid in MATLAB/Simulink, and its results have been analyzed and discussed. Simulations and numerical results show that for 96% of simulated topologies in single and multi-event faults, the relay settings are updated correctly and detect subsequent faults at the right time. Also, due to the use of offline calculations in the equipment layer, the time delay due to sending information to higher layers is minimized for single-event faults in the micro-grid. (C)& nbsp;2022 Published by Elsevier Ltd.

Abstract
ABSTR A C T Since energy hubs meet the needs of customers for different energies, their construction rate has increased in recent years. The annual growth of load demand on the one hand and the declining efficiency of hub converters on the other hand have posed many challenges for hub designers. Therefore, this study develops a multi-objective model for the design of hub considering converters' variable efficiency, degradation of equipment and annual growth of the load and energy prices. The proposed hub is equipped by a power-to-gas (P2G) technology and its consumers participate in an integrated demand response (IDR) program. The problem is formulated in mixed-integer non-linear programming (MINLP) format and is solved via DICOPT in GAMS environment. The simu-lation results substantiate that dynamic framework has led to the much more accurate determination of equipment capacity. Besides, the results indicate that the P2G technology reduces CO2 emissions by 9.89% through consuming CO2 emitted from the CHP and boiler. The results also illustrate that P2G increases the ef-ficiency of gas-fired converters by injecting hydrogen into them, thus reducing losses by 9.2%.

Abstract
Electric springs (ESs) have proven effective for integrating renewable generation into power systems. An ES connected in series with a non-critical load forms a smart load whose consumption can be dynamically controlled for voltage regulation and demand side management. In most existing applications, smart loads have been devoted to providing services to the grid without accounting for their own interests. The novelty of this paper is to propose a price-based strategy to coordinate the operation of multiple ESs in microgrids. Smart loads consisting of ESs connected to electric water heaters are modeled as rational agents that locally optimize their own objectives by adjusting their consumption schedules in response to price/control signals. Such signals are determined at the microgrid central controller (MGCC) when solving the microgrid operation scheduling problem formulated to minimize the microgrid operation cost taking into account the smart loads' consumption schedules. An iterative optimization algorithm determines the equilibrium between the microgrid and smart loads' objectives requiring only the exchange of price/control signals and power schedules between the local controllers and the MGCC. Case studies show the effectiveness of the proposed strategy to economically benefit both the microgrid and smart loads when scheduling their operation.