Journal of Energy Management and Technology
Volume:6 Issue: 4, Autumn 2022

The paper establishes a framework for finding the optimal inputs into complex systems with higher energy intensity. It has a lower computational load, higher reliability, and better accuracy. The first is carried out through the developed linearization algorithm of a system. There is the ability to match every output to input to reach reliability, and a sophisticated algorithm guarantees accuracy. An Electric Arc Furnace model is chosen to validate the framework because of its nonlinear functions, complexity, and significant energy intensity. The procedure is applied to an EAF model. At the step’s end, liquid mass, liquid temperature, and liquid grade must reach the desired ranges. The step is to be accomplished at the lowest cost in a determined time. The technique linearizes the nonlinear model around an operating point in the first step and reduces the system’s order. A suitable pairing is offered based on minimum interaction and passing some necessary decentralized integral controllable requirements in the second step. The third step is based on discretizing the operating point’s linearized system. This algorithm is iterated around new operating points. A comparison between the nonlinear system and reduced linear systems with the same feeds will be made in any iteration. If the results are compatible with each other, the next optimum feeds are estimated. Otherwise, the sample time decreases, and the loop should be repeated for the previous point. The study is performed on a well-known EAF model with 14 state variables and seven input variables. The model’s outcome also is.

Improvement of air quality and provision of the residents’ comfort in different buildings are the main tasks of HVAC (heating, ventilating, and air conditioning) systems. A large number of control methods have been applied to HVAC systems to adjust the indoor temperature in buildings and, at the same time minimizing energy consumption to be reflected in minimizing energy cost, furthermore, it reduces the peak load of the power grid, and provide ancillary services such as frequency regulation. This paper reviews different techniques proposed for modeling HVAC systems. Moreover, it provides a comprehensive review of HVAC system control methods, categorizes them, and then extracts their advantages, disadvantages, and main features. Furthermore, an HVAC system model is proposed and compared to the RLF (residential load factor) model with and without the Takagi-Sugeno Fuzzy (TSF) controller, which is applied to control the proposed and RLF HVACs. The results of the proposed HVAC model and the complete RLF model are compared from different aspects. The results demonstrate the efficiency and robustness of the proposed model. The energy consumption of the proposed HVAC system and RLF models, by applying TSF controller along a day are evaluated. The results show that the proposed HVAC system model is saving energy around 10.06% when compared with the RLF model.

Increasing the number of distributed generation resources in the form of microgrids, in addition to improving the technical conditions of these networks, causes many economic benefits to producers and consumers. Using a combination of several microgrids as a cluster of microgrids improves the mentioned advantages, however, the main problem is to find the best schedule for microgrids. In this paper, a software called MLEMS is proposed for the planning and cost analysis of multi-microgrid systems. This open-source software is based on the Visual Basic programming language, in the form of macro modules. By using GAMS and Matlab, in addition to the day-ahead scheduling of microgrid operation, the best solutions are also found to minimize the operation cost of multi-microgrid systems. Different parts of the software are provided in the form of modular layers to perform a better energy management system in which the user can enter the information for each microgrid separately. By designing the multi-microgrid system, modeling, optimization, and planning will be provided for any users in the software environment. A case study is performed to optimize the operating costs; using the proposed software for a multi-microgrid system and after several analyses, the best solution is given to the microgrid user. The Lindo solver has been presented the lowest solve time for the sample multi-microgrid system in the shortest solution time of 0.25 seconds. The cost of operating the sample system for a 24-hour has been calculated $ 520 by exposure to the operation plan in the MLEMS.

Water desalination using solar steam generation systems (SSGSs) is a facile and inexpensive technology that employs a renewable and environmentally friendly source of energy. In this method that solar is directly used for generating steam, a photothermal layer absorbs sunlight and converts it to heat, leading the evaporation of the water transferred toward this layer. Studies show that there are four main challenges in solar steam generation systems that should be considered in research and addressed by providing appropriate solutions. These challenges include managing and preventing heat loss, structural strength, managing and transferring water within the structure, absorbing light, and converting light into heat. In this paper, single-layer SSGSs composing of open porosity polyurethane (PU) foam mixed with photothermal materials are used to generate steam. Among the different systems comprising graphite, graphene oxide, carbon nanotube, char, and gold thin film, gold thin film and carbon nanotube based systems resulted the highest performances with an efficiency of more than 60% and the water evaporation rates of 0.824 and 0.808 Kg.m-2.h-1, respectively. Fortunately, the device shows no significant changes in its performance after 40 cycles, revealing the suitable stability.

A new comparative study between different district heating scenarios fed by a combined power plant from an energy and exergy point of view has been presented in this research. The proposed scenarios provide the flexibility of the power plant cycle in different environmental and geographical conditions to produce balanced electricity and heat. The proposed scenarios have been divided into design and off-design conditions when the maximum electricity or heat is the purpose of system operation. Results show that by relying on off-design scenarios, affordable outcomes are achievable. The heat efficiency in an off-design scenario has increased more by than 30% compared with the base case by controlling the exact demands on the equipment in the maximum power generation scenario. The total energy efficiency varies between 56.6 to 87.6 in different scenarios. It has been similarly observed for the energy and exergy efficiencies by growing 2% in the maximum heat production scenario. In addition, preparing a balance between the highest power of heat production has been fulfilled by proposing a medium model that can be supplied in an acceptable range. Eventually, the highest value of exergy destruction has been observed in the combustion chamber of the gas cycle (about 68%) due to thermochemical irreversibility.

Recently, distribution network planners have enacted some facilities and policies to utilize the potential of private investor participation. Network planners should propose an attractive scheme to persuade the investor to take part in the long-term planning. In this paper, a distribution network planning approach with the cooperation of the private investor is proposed. In the proposed approach, the network planner optimizes the battery energy storage systems (BESS) installed by the investor to satisfactorily shave the peak load of the system. Through this optimization, the planner provides a financial resource to support the investor during the planning horizon. The benefits of both participants are considered and evaluated through economic indices such as payback period years (PPY), profit investment ratio (PIR), internal rate of return (IRR), and net present value (NPV). Due to the presence of photovoltaic (PV) in the system, and the inherent intermittency of load, a K-means data clustering algorithm is employed to catch the uncertainty of the problem. The obtained mixed-integer nonlinear model is solved via particle swarm optimization (PSO) and the proposed approach is tested and implemented on a 16-bus distribution test system. A sensitivity analysis on the incentive price and investment cost is also performed. Finally, the obtained results are compared with the incentive price of several countries, and it is shown that the proposed approach leads to an acceptable result and reasonable incentive price, while the planner's targets are considered as well.

Smart home energy management is a useful tool to optimally manage the energy devices of a dwelling. A building with renewable units, controllable appliances, and the electric vehicle has the ability to implement home energy management. This study presents a novel multi-objective method for the smart home energy management system during the different seasons. The smart home has controllable and uncontrollable appliances while the rooftop photovoltaic panel can supply part of the demand during the day. The considered private electric vehicle has the vehicle-to-home technology for better participation in the home energy management program. The solar irradiance, state of charge, and availability of the electric vehicle in the parking are the uncertain parameters that are calculated using the combination of Latin hypercube sampling and K-means algorithms. The defined multi-objective technical-economic function is optimized using the dragonfly algorithm and then the best solution is selected using the fuzzy mechanism. The considered multi-objective algorithm is compared with other optimization methods for showing its efficiency. The numerical results, which are the output of implementing the method in a sample smart home, show the proper performance of the proposed method rather than other algorithms by about 10-40 %. Although the proposed method considerably improves the indices of the smart home, the highest efficiency of the smart home is achieved after applying the proposed energy management method on a spring day because of more availability of domestic energy units.