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Distillation has relatively low thermodynamic efficiency, so it is a prime target for process intensification studies. The current research aims to study exergy losses in various heat-integrated distillation columns. A conventional industrial-scale i-butane/n-butane fractionator has been selected as a case study for the comparison of the performances of various heat-integrated designs. The Aspen Plus® process simulator is used to perform steady-state simulations and exergy analyses of the conventional distillation column (CDC), internally heat-integrated distillation column (iHIDiC), externally heat-integrated double distillation columns (EHIDDiC), and vapor recompression (VRC) systems. The results of these exergy analyses show that a modified VRC system (ηE = 10.69%) is the most efficient design for this separation. The exergy efficiency of the conventional VRC system is the same as that of the CDC (ηE = 9.27%). The EHIDDiC system (ηE = 9.77%) is somewhat better than the CDC, whereas iHIDiC shows poor exergy efficiency (ηE = 8.09%), even lower than the CDC.

This paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66%case−1, 32.76%case−2, 26.46%case−3), 33 bus (33.09% case−1, 27.06%case−2, 24.15%case−3), and 69 bus (34.51% case−1, 29.43%case−2, 25.83%case−3) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique.

With the negative climate impact of fossil fuel power generation and the requirement of global policy to shift towards a green mix of energy production, the investment in renewable energy is an opportunity in developing countries. However, poor economy associated with limited income, funds availability, and regulations governing project funding and development are key factors that challenge investors in the energy sector. Given the various power generation resources, including renewables, it is necessary to evaluate the possible power generation investment options from an economic perspective. To realize this objective, solar PV, wind and diesel power generations are economically compared, considering the incremental rate of return and incremental benefit to cost ratio techniques. The alternative investment options of distributed generation technologies are evaluated for Maharashtra, India under different depreciation methods, and the effect of the latter on selecting the best investment candidate is investigated. The paper also conducts sensitivity analysis to examine the impact of capital cost, operation and maintenance cost, and fuel cost variations on the selection decision considering a comparison of the different general projects’ cash flow structures discussed in the literature. The economic aspects of selecting a project among possible alternatives for an investment in the power sector are analyzed, and the presented review provides comprehensive comparisons with respect to the literature approaches. The results reveal that, in the benchmark case study, the PV project is rejected and disregarded from further comparisons with other candidate projects since its equity internal rate of return (10.25%) is less than the minimum accepted rate of return, leaving the selection between wind and diesel energy projects. The study reveals that the incremental rates of return under such a comparison are 37.88%, 45.94% and 37.50% when MACRS, declining balance and straight line depreciations techniques are applied, respectively. Thus, the wind energy project is the favored option in this case. For the economic assessment of other case studies, the application of both sensitivity analysis on the capital cost and operation and maintenance cost and literature approaches to structure the projects reveal that wind energy for Maharashtra, India is a more attractive and feasible option compared to other distribution generation projects, while diesel is only considered to be a good option when its fuel cost is reduced by 5%. Finally, the paper highlights policy implications that can influence the decision to move towards investment in distributed generation technologies as a future research direction.

This paper is focused on the channel modeling techniques for implementation of narrowband power line communication (NB-PLC) over medium voltage (MV) network for the purpose of advanced metering infrastructure (AMI). Three different types of models, based on deterministic method, statistical method, and network parameters based method are investigated in detail. Transmission line (TL) theory model is used to express the MV network as a two-port network to examine characteristics of sending and receiving NB-PLC signals. Multipath signal propagation model is used to incorporate the effect of multipath signals to determine the NB-PLC transfer function. A Simulink model is proposed which considers the values of MV network to examine the characteristics of NB-PLC signals. Frequency selectivity is also introduced in the impedances to compare variations and characteristics with constant impedances based MV network. A state-of-the-art mechanism for the modeling of capacitive coupling device, and impedances of low voltage (LV) and MV networks is developed. Moreover, a comparative analysis of TL theory and multipath signal propagation models with the proposed Simulink model is presented to validate the performance and accuracy of proposed model. This research work will pave the way to improve the efficiency of next-generation NB-PLC technologies.

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort.

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.

In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).