As emerging marine technologies lead to the development of new infrastructure across the ocean, they enter an environment that existing ecosystems and industries already rely on. Although necessary to provide sustainable sources of energy and food, careful planning will be important to make informed decisions and avoid conflicts. This paper examines several techniques used for marine spatial planning, an approach for analyzing and planning the use of marine resources. Using open-source software including QGIS and Python, the potential for developing offshore aquaculture farms powered by a reference model wave energy converter from the Sandia National Labs, the RM3, along the Northeast coast of the United States is assessed and several feasible sites are identified. The optimal site, located at 43.7 degrees N 68.9 degrees W along the coast of Maine, has a total cost for a 5-pen farm of $56.8M, annual fish yield of 676 tonnes, and a levelized cost of fish of $9.23 per kilogram. Overall trends indicate that the cost greatly decreases with distance to shore due to the greater availability of wave energy and that conflicts and environmental constraints significantly limit the number of feasible sites in this region.

Renewable energy systems have been introduced into Microgrids (MGs) due to environmental issues and fossil fuel depletion. This paper presents a new management methodology to find the optimum operation of a grid-connected MG, which is modeled as an optimization approach and aims to minimize total cost. The uncertainties in renewable energy-based Distributed Generation (DG) units, including Wind Turbine (WT) and Photovoltaic (PV), are also considered in this study. The balance between total electricity generation and required demand in the system is determined based on the power interchange between the MG and the distribution system. The optimization problem is formulated, where its objectives include minimization of total operational and planning cost of the MG system, improving the voltage profile, and reducing total power losses of the distribution system. The presented methodology is applied on a 13-bus network, and the results are compared under different conditions. The simulation results demonstrate that the proposed methodology can supply all required electricity and simultaneously optimize power transactions between MG and the main network. Furthermore, it is indicated that the optimal power transaction between the main network and MG can reduce the distribution network's total operational cost. Meanwhile, reducing total power losses (i.e., more than 30 %) and improving the studied network's voltage profile results in a grid-connected mode.

Wind energy, offshore aquaculture, and wave energy developers are entering an already crowded space in the ocean, with various groups vying for its bounty. Constructive engagement between new developers, coastal communities, and fisheries will become increasingly necessary in order to minimize conflict and increase production. Marine spatial planning (MSP) is a technique used to make informed decisions about the sustainable use of marine resources. Offshore developers have begun to use MSP in order to achieve these goals of minimized conflicts, increased production, and greater sustainability. In this paper, an open-source MSP tool is developed to plan a favorable deployment location for a wave-powered aquaculture farm (WPAF) growing Atlantic salmon in the Northeast U.S. The MSP tool finds an optimal deployment location for the WPAF, while avoiding conflicts with existing or planned offshore activities, including recreational and commercial fisheries, military zones, offshore wind farms, shipping lanes, and marine protected areas. The planning objective is to find a deployment location where the total cost of weekly vessel travel to a WPAF and the cost of the wave energy converters (WECs) used to power the farm is minimized. In addition, this deployment location must meet the environmental needs of fish, have the required wave environment for sufficient wave energy production to supply the aquaculture farm, and avoid conflicts with the aforementioned industries. To solve this MSP optimization problem, we use a brute-force searching method through a coarse grid in the Northeast U.S. to find the optimal deployment location. The MSP tool is tested for multiple sizes of an Atlantic salmon offshore aquaculture farm: small (5 net pens), medium (12 net pens), and large (40 net pens), and the power needs of the farm are supplied by a two-body point-absorber WEC (the Department of Energy Reference Model 3). The optimal deployment location is found to be the same for all sized farms at 44.1°N and 67.9°W, about 35km off the Coast of Acadia National Park, since this location has the highest wave power density and hence minimizes the number of WECs needed to power each farm.

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. To enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

This paper presents a reinforcement learning (RL) framework applied for an autonomous underwater vehicle (AUV) path planning, focusing on a specific type of energyharvesting AUV, entitled marine current turbine (MCT). The proposed RL-based approach improves a classical path planning to adopt with an underwater environment prone to spatiotemporal uncertainties. The path planning problem is formulated to achieve the goal of maximizing the harnessed energy from the MCT subject to the agent dynamics and the spatiotemporal environment constraints. Three RL algorithms, including Q-learning, deep Q-network (DQN), and proximal policy optimization (PPO), are nominated to deal with the path planning over both discrete gridded and continuous underwater environments modeling. The experimental results demonstrate the efficiency of the RL-based approaches in seeking the optimal path in the underwater environment, where further discussion is presented to generalize the proposed approach to other energy-harvesting autonomous vehicles operating in the spatiotemporally varying environment, such as airborne wind turbines.

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the ν-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework's effectiveness is shown with the renewable power generation improvement during a faulty case.

This article presents a novel spatiotemporal optimization approach for vertical path planning (i.e., waypoint optimization) to maximize the net output power of an ocean current turbine (OCT) under uncertain ocean velocities. To determine the net power, OCT power generation from hydrokinetic energy and the power consumption for controlling the depth are modeled. The stochastic behavior of ocean velocities is a function of spatial and temporal parameters, which is modeled through a Gaussian process (GP) approach. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are applied to solve the formulated spatiotemporal optimization problem with constraints. Comparative studies show that the MPC- and RL-based methods are computationally feasible to address vertical path planning, which are evaluated with a baseline A* approach. Analysis of the robustness is further carried out under the inaccurate ocean velocity predictions. Results verify the efficiency of the presented methods in finding the optimal path to maximize the total power of an OCT system, where the total harnessed energy after 200 h shows over an 18% increase compared to the case without optimization.

This paper addresses the optimal sizing of renewable energy systems (RESs) in a microgrid (MG), where the MG participates in the electricity market. A novel method for reliability analysis is proposed in this study to deal with the high penetration of RESs. In this framework, the MG is considered as a price maker, having a two-direction relation with the electricity market. RESs, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cells, are optimally sized based on the reliability index, and the results are evaluated before and after the MG involvement in the electricity market. The results show a 3.6% decrease in the total cost of the microgrid as a result of the transactions with the electricity market. Furthermore, the efficiency of the proposed approximate reliability method is verified, where the reliability of the MG is evaluated with less computational complexity and acceptable accuracy.

Recent research progress has confirmed that using advanced controls can result in massive increases in energy capture for marine hydrokinetic (MHK) energy systems, including ocean current turbines (OCTs) and wave energy converters (WECs); however, to realize maximum benefits, the controls, power-take-off system, and basic structure of the device must all be co-designed from early stages. This paper presents an OCT turbine control co-design framework, accounting for the plant geometry and spatial-temporal path planning to optimize the performance. Developing a control co-design framework means that it is now possible to evaluate the effects of changing plant geometry on a level playing field when accounting for the OCT plant power optimization. The investigated framework evaluates the key design parameters, including the sizes of the generator, rotor, and variable buoyancy tank in the OCT system, and formulates these parameters' effect on the OCT model and harnessed power through defining a power-to-weight ratio, subject to the design and operational constraints. The control co-design is formulated as a nested optimization problem, where the outer loop optimizes the plant geometry and the inner loop accounts for the spatial-temporal path planning to optimize the harnessed power with respect to the linear model of the OCT system and ocean current uncertainties. Compared with a baseline design, results verify the efficacy of the proposed framework in co-designing an optimal OCT system to gain the maximum power-to-weight ratio.

This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem constraints. The planner and the tracking control are accommodated in an integrated framework to optimize these two parts in a real-time manner. The proposed approach is validated on a marine current turbine (MCT) that executes vertical waypoint path searching to maximize the net power due to spatiotemporal uncertainties in the ocean environment, as well as the path following via an MPC tracking controller to navigate the MCT to the optimal path. Results demonstrate that the path planning increases harvested power compared to the baseline (i.e., maintaining MCT at an equilibrium depth), and the tracking controller can successfully follow the reference path under different shear profiles.

Optimization of Economic Power Flow in DC Microgrid in the Presence of Renewable Energy Resources with Voltage Controller Using Virtual Impedance and Real-time Pricing

This paper presents a stochastic management algorithm to address the optimal operation of smart microgrids (MGs) in the high presence of stochastic renewable energy resources. The proposed management algorithm aims to find a day-ahead optimal operation of the renewable energy resources, including wind turbine, photovoltaic (PV) unit, Fuel Cell (FC), electrolyzer, microturbine, and energy storage that simultaneously considers the participation of the smart homes in demand response programs. The presented stochastic management is formulated as a mixed-integer linear programming (MILP) problem solved through a stochastic programming approach. Two different energy storage devices, i.e., battery and hydrogen storage tank, are modeled, and their operations are compared. Various uncertainties (e.g., wind speed, solar irradiance, demand, and electricity price) are modeled, where the scenario-based approach is applied to find a finite number of scenarios. The efficiency of the presented algorithm is verified in four scenarios to evaluate the effect of hydrogen storage and demand response program. Finally, it is shown that applying both hydrogen storage and demand response program can benefit the MG from the economic perspective and results in a significant reduction in the total cost.

This paper presents a predictive approach to address real-time vertical path planning for a marine current turbine (MCT) treated as an autonomous underwater vehicle (AUV), where the path control goal is to maximize the total harvested ocean current energy. The real-time path planning is formulated as a sequence of optimization problems over a prediction horizon with respect to the autonomous MCT model and underwater environment model. The ocean current velocity is modeled through a spatiotemporal neural network (STNN) trained using field-collected acoustic Doppler current profiler (ADCP) data. Model predictive control (MPC)-based approach is proposed to solve the optimizations, where the proposed approach takes advantage of fast discrete path planning (i.e., path planning in a gridded ocean environment) to seek the initial solution, as well as continuous path planning to improve the initial solution in a continuous ocean environment. Results demonstrate that the proposed reinforced continuous path planning algorithm can find a better solution (i.e., optimal path) than independent continuous path planning.

This paper presents a scenario-based stochastic management algorithm to deal with scheduling the optimaloperation of a multi-carrier microgrid (MG). The proposed framework of the multi-carrier MG comprises ofwind turbine (WT), photovoltaic (PV) panel, fuel cell (FC), microturbine (MT), boiler, combined heat andpower (CHP) unit, electrical load, thermal load, hydrogen load, electrical energy storage, H2 storage, andthermal storage. To cope with the uncertainties arisen by the renewable resources, electricity price, electricalload, and electric vehicle (EV), we proposed a scenario-based approach to model the uncertain parameters(wind speed, solar irradiance, electricity price, electrical load, EV arrival time, EV departure time, and EVtraveled distance). The proposed algorithm initially estimates the uncertain parameters through fitting aprobability distribution function (PDF) with the time series of historical uncertain parameters. Then, thescenario generation and reduction approach is applied to find the finite scenarios according to the fitted PDFs.The stochastic management algorithm is formulated as a cost minimization problem, where the demandresponse (DR) program is embedded in the formulation. The proposed framework is tested on a samplemulti-carrier MG for two cases, namely, the application of stochastic energy management for a Multi-carrierMG with and without DR. Also, we perform a comparative study to show the benefits of the stochasticmanagement algorithm along with a deterministic approach.

Increasing the implementation of distributed generation and introducing multi-carrier energy systems highlight the need for energy hub systems. The energy hub is a new idea implemented in multi-carrier energy systems, sending, receiving, and storing different energy types. Therefore, the present paper proposes an improved energy hub consisting of different types of renewable energy-based DG units considering electricity and heating storage systems, which models the system’s operation and planning aspects. Furthermore, optimal planning and scheduling of multi-carrier energy hub system is modeled considering the stochastic behavior of wind and photovoltaic units. The operation section’s main challenge is determining the optimal interaction between different resources for supplying other loads in the system. The presented model is solved using a robust method based on a Quantum Particle Swarm Optimization (QPSO) approach to minimize the energy hub system’s total cost. The minimization of fuel consumption and pollutant emissions due to implementing the residential energy hub’s thermal storage system is evaluated. Simulation results show that the amount of consumed natural gas reduces by 48% after using CHP units produced heat to supply heating and cooling loads. After installing CHP and thermal storages in the energy hub system, the amount of CO2 has reduced by about 904 tons during a year. It can be concluded that the produced power of CHP is at the highest, which is equal to 61%, as it can generate electricity at all times during the day. Moreover, to evaluate the efficiency of the proposed methodology, the Genetic Algorithm (GA) and PSO algorithm are also implemented for optimization of the mentioned energy hub system. The performance of the mentioned algorithms is compared with each other, and the results depicted that the QPSO algorithm is the best and the convergence speed and global search ability of the QPSO algorithm are significantly better than PSO and GA algorithms The obtained numerical results verify the efficiency of the proposed method in the optimal scheduling and planning of the energy hub system in the presence of stochastic renewable energy systems.

Stochastic energy management of smart microgrids (MGs) is an important subject due to the high integration of intermittent resources, including wind turbine (WT) and photovoltaic (PV) units. The complexity of the multi MGs management algorithm increases, considering their participation in an electricity market. In this paper, we proposed a stochastic energy management algorithm to address the participation of smart MGs in the electricity market, which minimizes the total cost and finds the optimal size of different components, including WT, PV unit, fuel cell, Electrolyzer, battery, and microturbine. The intermittencies in the PV output power, WT output power, and electric vehicle (EV) are modeled and integrated into the management algorithm using the Copula method. The market clearing price (MCP) is found using a game theory (GT) model and Cournot equilibrium. To verify the efficiency of the proposed method, it is tested on a sample three-MG, where the optimal size of various components is obtained. The obtained results verify that the total cost of MG decreases and the better performance can be obtained after participation in the electricity market. A sensitivity analysis is also done to evaluate the effects of various parameter changes (e.g., capital cost, replacement cost, and operation and maintenance cost) in various scenarios, where the obtained results verify that the cost reduction is obtained over different scenarios.

The plug-in hybrid electric vehicles (PHEVs) integration into the electrical network introduces new challenges and opportunities for operators and PHEV owners. On the one hand, PHEVs can decrease environmental pollution. On the other hand, the high penetration of PHEVs in the network without charging management causes harmonics, voltage instability, and increased network problems. In this study, a charging management algorithm is presented to minimize the total cost and flatten the demand curve. The behavior of the PHEV owner in terms of arrival time and leaving time is modeled with a stochastic distribution function. The battery model and hourly power consumption of PHEV are modeled, and the obtained models are applied to determine the battery's state of charge. The proposed method is tested on a sample demand curve with and without a charging management algorithm to verify the efficiency. The results verify the efficiency of the proposed method in decreasing the total cost using the management algorithm for PHEVs, especially when the PHEVs sell the electricity to the network.

This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.

Different types of renewable energy resources can be used as a replacement for the fossil fuel-based powerplants. In addition, these resources can be directly installed near consumers as distributed generations, which decrease their dependency on main network. At the same time, combined heat and power (CHP) units and boiler are applied for supplying thermal demand of residential sectors. As a result, an energy management algorithm is presented in this chapter to use all capacities of renewable energy systems, thermal resources, and energy storages in order to supply both electrical and thermal demand of residential communities. Wind turbine and photovoltaic units as renewable energy resources and battery are modeled in terms of power production and cost. Further, thermal resources, including CHP and boiler are also modeled to meet thermal demand. The presented energy management algorithm aims to supply all residential loads, while total cost and its dependency on main network are simultaneously minimized. Hence, all shortages in supplying demand are first purchased from other residential communities and second from main network. The obtained results verify efficiency of the presented method in supplying all residential demand, which minimizes total cost and decreases pollution emission at the same time.