Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOptimization of vertically mounted agrivoltaic systems
294
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Agrivoltaic systems represent a key technology for reaching sustainable development goals reducing the completion of land for food versus land for energy. Moreover, agrivoltaic systems are at the centre of the nexus between electricity production, crop production, and irrigation water saving. In this study, an optimization model for vertically mounted agrivoltaic systems with bifacial photovoltaic models is developed. The model combines three main submodels: solar radiation and shadings, photovoltaic, and crop yield. Validation of the submodels is performed showing good agreement with measured data and commercial software. The optimization model is set as multi objective to explore the trade-offs between competing agrivoltaic key performance indicators. The results shows that the row distance between bifacial photovoltaic modules structure affects significantly the photosyntetically active radiation distribution by reducing the crop yield of potato and oat of about 50% by passing from 20 m to 5 m. The implementation of agrivoltaic system for the investigated crops at the chosen location shows a land equivalent ratio above 1.2 that justify the technology for reaching the country sustainability goals.
rate research
Read More
We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-$8$ flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (GP). Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent basis parameters. Once a path is generated using Bayesian optimization, a path following mechanism is used to track the generated figure-$8$ flight. The proposed framework is validated on a simplified $2$-dimensional model that mimics the key behaviors of a $3$-dimensional AWE system. We demonstrate the capability of the proposed framework in a simulation environment for a simplified $2$-dimensional AWE system model.
This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DQN environment. Finally, the proposed multi-objective MADRL method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging time-variant local linear-quadratic approximations of system dynamics and reward, such methods can find both a target-optimal trajectory and time-variant optimal feedback controllers. However, the local linear-quadratic assumptions are a major source of optimization bias that leads to catastrophic greedy updates, raising the issue of proper regularization. Moreover, the approximate models disregard for any physical state-action limits of the system causes further aggravation of the problem, as the optimization moves towards unreachable areas of the state-action space. In this paper, we address the issue of constrained systems in the scenario of online-fitted stochastic linear dynamics. We propose modeling state and action physical limits as probabilistic chance constraints linear in both state and action and introduce a new trajectory optimization technique that integrates these probabilistic constraints by optimizing a relaxed quadratic program. Our empirical evaluations show a significant improvement in learning robustness, which enables our approach to perform more effective updates and avoid premature convergence observed in state-of-the-art algorithms.
This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex non-convex mixed integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum-rate of users via optimizing the LED selection with a given precoding matrix, is a mixed integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency of TASP-HD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than AD and DD, respectively.
The widespread diffusion of distributed energy resources, especially those based on renewable energy, and energy storage devices has deeply modified power systems. As a consequence, demand response, the ability of customers to respond to regulating signals, has moved from large high-voltage and medium-voltage end-users to small, low-voltage, customers. In order to be effective, the participation to demand response of such small players must be gathered by aggregators. The role and the business models of these new entities have been studied in literature from a variety of viewpoints. Demand response can be clearly applied by sending a dedicated price signal to customers, but this methodology cannot obtain a diverse, punctual, predictable, and reliable response. These characteristics can be achieved by directly controlling the loads units. This approach involves communication problems and technological readiness. This paper proposes a fully decentralized mixed integer linear programming approach for demand response. In this framework, each load unit performs an optimization, subject to technical and user-based constraints, and gives to the aggregator a desired profile along with a reserve, which is guaranteed to comply with the constraints. In this way, the aggregator can trade the reserve coming from several load units, being the only interface to the market. Upon request, then, the aggregator communicates to the load units the modifications to their desired profiles without either knowing or caring how this modification would be accomplished. The effectiveness is simulated on 200 realistic load units.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
