Various demand side management techniques and its role in smart grid–the state of art

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

Optimal Operation of Automotive Electrical System with Photovoltaic Generation and Three-level Battery Management Scheme

For many years, the electrical power requirements in Automotive Electrical System (AES) have been quickly increasing and are predicted to continue to climb. This trend is being pushed by the introduction of a slew of new vehicle features. The constant growth in power needs is stretching the limitations of current automotive power generation and control technologies, stimulating the development of higher-power and higher-voltage electrical systems and components. Electrical power on a vehicle is not free. It comes as a direct result of consuming fuel within the engine to drive the alternator. With a typical engine efficiency of 44% and with present fuel costs this leads to onboard electrical power costs 4 times more than a typical household utility rate. Global oil and gas resource depletion, as well as environmental concerns, have prompted the automobile industry to build more efficient and eco-friendly cars in order to minimize fuel use and safeguard the environment. In our proposed Automotive Electrical system configuration, we have an AES system which is powered by an automotive alternator and battery combination where the alternator is driven by an IC engine and we have a hybrid energy system using a Rooftop PV array with a battery management system (BMS). We discovered that during the off state, the whole load of the automobile is dependent on the 12Vlead acid battery for power, which causes the SOC to drop dramatically. As a result, the suggested model will include a flexible thin-film solar PV module positioned on the rooftop, which will be supported by a Maximum Power Point (MPPT) Tracking charge controller and will deliver energy to recharge the extra battery and meet the electrical requirements when the vehicle is stationary. When the vehicle is in motion, the existing alternator in the car's electrical system takes over the battery charging requirements, by this way, we can meet the electrical requirements of AES without running the engine for a long time by consuming fuel. The proposed model specialty is investigated using MATLAB/Simulink and compared with existing methods. Keywords: Automotive Electrical System (AES), Internal Combustion Engine (ICE), Hybrid Energy System, Rooftop PV array, Maximum Power Point Tracking (MPPT).

Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China

Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential.

Evaluation of the Potential of Nanofluids Containing Different Ag Nanoparticle Size Distributions for Enhanced Solar Energy Conversion in Hybrid Photovoltaic-thermal (PVT) Applications

Hybridising photovoltaic and photothermal technologies into a single system that can simultaneously deliver heat and power represents one of the leading strategies for generating clean energy at more affordable prices. In a hybrid photovoltaic-thermal (PVT) system, the capability to modulate the thermal and electrical power output is significantly influenced by the spectral properties of the heat transfer fluid utilised. In this study, we report on one of the first experimental evaluations of the capability of a multimodal silver nanofluid containing various particle shapes and particle sizes to selectively modulate the solar energy for PVT applications. The diverse set of particle properties led up to a 50.4% enhancement in the solar energy absorbed by the nanofluid over the 300 nm – 550 nm spectral region, where silicon is known to exhibit poor photovoltaic conversion performances. This improved substantially the absorption of solar energy, with an additional 18 – 129 W m-2 of thermal power being generated by the PVT system. Along with the advancements made in the thermal power output of the PVT system, a decrease of 4.7 – 36.6 W m-2 in the electrical power generated by the photovoltaic element was noted. Thus, for every ~11 W m-2 increase of thermal power achieved through the addition of the nanoparticles, a reduction of ~3 W m-2 in the ability to generate clean electricity was sustained by the PVT. Despite the energy trade-offs involved under the conditions of the nanofluid, the PVT system cumulatively harvested 405 W m-2 of solar energy, which amounts to a total conversion efficiency of 45%. Furthermore, the economics of the additional energy harvested through merging of the two systems was found to reach an enhancement of 77% under certain European conditions.

Cu-Ni-Based Alloys from Nanopowders as Potent Thermoelectric Materials for High-Power Output Applications

A new approach for the development of thermoelectric materials, which focuses on a high-power factor instead of a large figure of merit zT, has drawn attention in recent years. In this context, the thermoelectric properties of Cu-Ni-based alloys with a very high electrical conductivity, a moderate Seebeck coefficient, and therefore a high power factor are presented as promising low-cost alternative materials for applications aiming to have a high electrical power output. The Cu-Ni-based alloys are prepared via an arc melting process of metallic nanopowders. The heavy elements tin and tungsten are chosen for alloying to further improve the power factor while simultaneously reducing the high thermal conductivity of the resulting metal alloy, which also has a positive effect on the zT value. Overall, the samples prepared with low amounts of Sn and W show an increase in the power factor and figure of merit zT compared to the pure Cu-Ni alloy. These results demonstrate the potential of these often overlooked metal alloys and the utilization of nanopowders for thermoelectric energy conversion.

Optimal Setting of Thyristor Controlled Series Compensator with Brain Storm Optimization Algorithms for Available Transfer Capability Enhancement

Applications of Flexible AC Transmission Systems (FACTS) devices for enhancement of Available Transfer Capability (ATC) is gaining attention due to economic and technical limits of the conventional methods involving physical network expansions. FACTS allocation which is sine-qua-non to its performance is a major problem and it is being addressed in recent time with heuristic algorithms. Brain Storm Optimization Algorithms (BSOA) is a new heuristic and predicting optimization algorithms which revolutionizes human brainstorming process. BSOA is engaged for the optimum setting of FACTS devices for enhancement of ATC of a deregulated electrical power system network in this study. ATC enhancement, bus voltage deviation minimization and real power loss regulation are formulated into multi-objective problems for FACTS allocation purposes. Thyristor Controlled Series Capacitor (TCSC) is considered for simulation and analyses because of its fitness for active power control among other usefulness. ATC values are obtained for both normal and N-1-line outage contingency cases and these values are enhanced for different bilateral and multilateral power transactions. IEEE 30 Bus system is used for demonstration of the effectiveness of this approach in a Matlab software environment. Obtained enhanced ATC values for different transactions during normal evaluation cases are then compared with enhanced ATC values obtained with Particle Swarm Optimization (PSO) set TCSC technique under same trading. BSO behaved much like PSO throughout the achievements of other set objectives but performed better in ATC enhancement with 27.12 MW and 5.24 MW increase above enhanced ATC values achieved by the latter. The comparative of set objectives values relative to that obtained with PSO methods depict suitability and advantages of BSOA technique.