Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

Runoff Prediction Based on the Discharge of Pump Stations in an Urban Stream Using a Modified Multi-Layer Perceptron Combined with Meta-Heuristic Optimization

Runoff in urban streams is the most important factor influencing urban inundation. It also affects inundation in other areas as various urban streams and rivers are connected. Current runoff predictions obtained using a multi-layer perceptron (MLP) exhibit limited accuracy. In this study, the runoff of urban streams was predicted by applying an MLP using a harmony search (MLPHS) to overcome the shortcomings of MLPs using existing optimizers and compared with the observed runoff and the runoff predicted by an MLP using a real-coded genetic algorithm (RCGA). Furthermore, the results of the MLPHS were compared with the results of the MLP with existing optimizers such as the stochastic gradient descent, adaptive gradient, and root mean squared propagation. The runoff of urban steams was predicted based on the discharge of each pump station and rainfall information. The results obtained with the MLPHS exhibited the smallest error of 39.804 m3/s when compared to the peak value of the observed runoff. The MLPHS gave more accurate runoff prediction results than the MLP using the RCGA and that using existing optimizers. The accurate prediction of the runoff in an urban stream using an MLPHS based on the discharge of each pump station is possible.

Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

Congestion management using grey wolf optimization in a deregulated power market

Transmission congestion issues became more severe and difficult to control as the power sector became more deregulated. The grey wolf optimization algorithm is proposed to relieve congestion by rescheduling generation effectively, resulting in the least congestion cost. The selection of participating generators is based on sensitivity, and the proposed technique is used to determine the best-rescheduled output active power generation to minimize line overload. The IEEE-30 bus system is used to test the proposed optimization technique. It has been demonstrated that when compared to other algorithms like the real coded genetic algorithm, particle swarm optimization, and differential evolution algorithm, the proposed approach produces excellent results in terms of congestion cost.

Studying effects of joint characteristics on the performance of a cantilever beam shaped piezoelectric energy harvester through joint identification

In energy harvesting systems, specifications of the generated electrical energy depend on the structure’s dynamics. This dependence can be used to identify the system’s joint characteristics. To this end, an innovative frequency-response-function (FRF) based identification method is presented. The investigated system is a cantilever beam shaped structure with an embedded bimorph piezoelectric bender, connected to a base via bolted joint as a depiction for wing of a UAV connected to fuselage. The implemented FRF is ratio of the piezoelectric output voltage to the base input displacement. The joint identification procedure consists of analytical modeling of the system with joint, experimental testing of the system and a real-coded Genetic Algorithm (GA) method. The joint is modeled as a combination of longitudinal and torsional springs, whose stiffnesses are obtained using the GA method. The obtained results indicate that the analytical model has good correlation with the experimental data. Then, effects of the joint characteristics on the energy harvester’s performance are investigated by comparison of the system with two different joint assumptions, namely, rigid and realistic joint. Finally, the effects of various joint characteristics on the energy harvester’s performance are presented and approaches to achieve the maximum performance of the system are suggested.

A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with PEVs (DED with PEVs) determines the optimal level of online units and PEVs, to minimize the fuel cost and grid fluctuations. Considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear, and non-convex characteristics. High efficient DED method provides a powerful tool in both power system scheduling and PEVs charging coordination. In this study, firstly, PEVs are integrated into the DED problem, which can carry out orderly charge and discharge management to improve the quality of the grid. To tackle this, a novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based on feature intervals (GADMFI), is proposed to enhance the exploitation and exploration of conventional RCGAs. Thirdly, a simple and efficient constraint handling method is proposed for an infeasible solution for DED. Finally, the proposed method is compared with the current literature on six cases with three scenarios, including only thermal units, units with disorderly PEVs, and units with orderly PEVs. The proposed GADMFI shows outstanding advantages on solving the DED with/without PEVs problem, obtaining the effect of cutting peaks and filling valleys on the DED with orderly PEVs problem.