As an important part of incentive demand response (DR), interruptible load (IL) can achieve a rapid response and improve demand side resilience. Yet, model-based optimization algorithms concerning with IL require the explicit physical or mathematical model of the system, which makes it difficult to adapt to realistic operation conditions. In this paper, a model-free deep reinforcement learning (DRL) method with dueling deep Q network (DDQN) structure is designed to optimize the DR management of IL under the time of use (TOU) tariff and variable electricity consumption patterns. The DDQN-based automatic demand response (ADR) architecture is firstly constructed, which provides a possibility for real-time application of DR. To obtain the maximum long-term profit, the DR management problem of IL is formulated as a Markov decision process (MDP), in which the state, action, and reward function are defined, respectively. The DDQN-based DRL algorithm is applied to solve this MDP for the DR strategy with maximum cumulative reward. The simulation results validate that the proposed algorithm with DDQN overcomes the noise and instability in traditional DQN, and realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.

Conventional electricity generation is one of the greatest sources of CO2 emissions. For a successful transformation of conventional energy systems into non-polluting and renewable energy systems, technology-focused traditional systems and economics must be combined for a more accurate holistic viewpoint with consideration of socio-political, technical, economic and environmental factors. Hybrid energy systems are considered the most feasible solution to the stochastic nature of renewable energy resources (RERs). Different renewable sources such as wind, solar, and hydrogen fuel cells can be integrated to form hybrid systems. An energy management strategy (EMS) is a strategy for power flow coordination among different components, by considering power demand and other constraints. The choice for an accurate EMS is the key element of a hybrid system as it is instrumental in providing an optimum solution of the hybrid system design and operation management. The objective of the optimization is to find suitable configurations for cost-effective solutions. Optimization and EMS must be treated as one entity to completely understand the system design. This study focuses on a techno-economic analysis with an optimized sizing of a hybrid renewable energy system (HRES) components to meet the residential load demand of a specific area in Pakistan. Nine different scenarios based on the PV-wind-diesel-BSS-converter system are investigated in terms of total net present cost (TNPC), Levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions to find the optimal system design. HOMER Pro software is used to develop the HRES model and for simulation analysis, with optimal sizing of each component for an economical solution. Simulation studies established that PV-wind-BSS-converter is the best suitable choice for the given location, and the optimal component sizes were determined. The TNPC of this system is $ 47,398 and the LCOE is $ 0.309/kWh. This represents an 81.7 % decrease in overall cost, compared to the base case (diesel only) and a 100% reduction in harmful gases while satisfying 100 % of the energy requirement with a 63.9 % of the surplus. MATLAB/Simulink model is developed for the optimum HRES system design. Its validity is tested by maintaining bus voltages (dc and ac), the secure operation range of storage SOC and real power balance among different components of the hybrid renewable energy system (HRES), and an effective ac voltage, irrespective of external perturbations. Model predictive control (MPC) is regarded as a high-performing algorithm. Since power converters are largely applied in microgrids (MGs), the problem formulation with MPC for a reconfigurable bidirectional voltage source converter (VSC) is applied in this work for hybrid MG. The inevitable fluctuations due to the linear and non-linear loads and the nature of renewable sources are addressed. The regulation of ac voltage is implemented through a finite control set model predictive control (FCS-MPC) based active front end (AFE) rectifier, while direct power MPC (DPMPC) is used to control the power during grid-connected operation. The regulation of an ac load voltage is done through voltage based MPC (MPVC) in the islanding operation of the MG. Moreover, the HRES transition from gridtied to grid-isolated mode is comprehensively analyzed. MATLAB/Simulink® software certified the robustness and evaluated the performance of the proposed HRES model under different varying loads viz. balanced, unbalanced, and non-linear. The proposed strategy offers superior performance with low total harmonic distortion (THD), compared to previously developed strategies. The output waveform of voltage and current have THD of 0.28 % compared to 3.71 % with the conventional strategy. The contributions of this paper lie in the sequential use of HOMER as well as MATLAB tools and in the validation of the suggested HRES plan for the considered location; along with the implementation of FCS-MPC for a reconfigurable bidirectional VSC.

Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with $ 28,620 NPC and 0.311 $/kWh COE which shows 81.65% reduction in cost and 100% preserving in toxic emission while fulfilling 100% energy demand with 67.3% of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30% as compared to 5.44% with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter.

The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

Standalone rural microgrid (MG) systems are considered as a sustainable and economical solutions towards rural area electrifications. Specific control schemes are necessary to adopt for reliable and economic performance of these rural MGs. This study focuses on the optimal utilization of biomass potential considering specific applications of bio generator (BG) with BG-PV-WT-BSS and BG-PV-SMES based standalone rural MG systems. In the first case of the BG-PV-WT-BSS, the optimal sizing/selection of DGs of a rural MG has been proposed using the improved-MILP (I-MILP) approach. The objectives of this study were to minimize total net present cost (TNPC), the levelized cost of energy (LCOE) and GHG emissions. In the second case of BG-PV-SMES, the simulation model of the rural microgrid consisting of a variable speed bio generator (VSBG) and photovoltaic (PV) has been developed. Afterwards, a simplified EMS has been designed for the coordinated operation control of the distributed energy resources (DERs) in the rural MGs using MATLAB/Simulink ^® environment. For the DGs connected via power converter, FOSMC and FCS-MPC based coordinated control has been proposed in the simplified EMS. The purpose of the FOSMC and FCS-MPC based power converter is the improvement of the system performance (for instance power quality, regulated voltage and THD) under external disturbances. Simulation analysis shows the better operation of FOSMC and FCS-MPC under less THD and improved power quality.