• Energy Research

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

Collaborative demand response management is an effective method to lower the peak-to-average ratio of demand and to facilitate the integration of locally distributed renewable energy resources to the electricity grid. The aggregator needs a holistic and privacy-preserving demand response management scheme to involve residential customers in a dynamic pricing market scenario. Using a quadratic function to model dynamic pricing, we propose a two-level distributed energy management scheme for a residential community to exploit the benefits of coordination among customers at the aggregator level and the smart devices at the customer level. In the proposed scheme, each customer wants to optimize the scheduling of its smart appliances, demand flexibility of air conditioning load, and energy storage strategies to minimize their expected cost, discomfort and appliance interruption. The aggregator, on the other hand, seeks to minimize the overall expected cost by optimizing customers energy demand and its energy storage strategies. The aggregator level optimization is formulated as a noncooperative Stackelberg equilibrium problem with shared constraints. Meanwhile, the customer level problem is formulated as a multiobjective optimization using different discomfort and interruption indicators to characterize various appliance preferences. We formulate iterative algorithms to obtain the appliance scheduling and storage strategies of the customers using genetic algorithm and to reach convergence. Simulation results indicate that the proposed scheme converges while enforcing the shared constraints and reduces the electricity cost to the customers with a quantifiable tradeoff between multiple objectives.

Neural networks in recent years have seen a rise in partial discharge related applications, with efforts mainly focused on measurements from ultra-high frequency sensors or high frequency current transformers. Existing works do not include neural network analysis of time-resolved partial discharge measurements on in-service cables generated with an external energising source. The inherent convoluted nature of these waveforms is a complicated recognition task which traditionally requires costly domain expert interpretation. This paper compares several neural network models and proposes a method that performs highly accurate recognition whilst reducing cost. The effectiveness of the proposed procedure is demonstrated by evaluating the performance across statistical measures.

We consider the problem of data collection from a network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favor a fair and energy-aware solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimization problem for energy harvesting mobile sensor network, that maximizes the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-theart. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

This paper mainly focuses on the energy management of microgrids (MGs) consisting of combined heat and power (CHP) and photovoltaic (PV) prosumers. A multiparty energy management framework is proposed for joint operation of CHP and PV prosumers with the internal price-based demand response. In particular, an optimization model based on Stackelberg game is designed, where the microgrid operator (MGO) acts as the leader and PV prosumers are the followers. The properties of the game are studied and it is proved that the game possesses a unique Stackelberg equilibrium. The heuristic algorithm based on differential evolution is proposed that can be adopted by the MGO, and nonlinear constrained programing can be adopted by each prosumer to reach the Stackelberg equilibrium. Finally, via a practical example, the effectiveness of the model is verified in terms of determining MGO's prices and optimizing net load characteristic, etc.

This paper investigates the fee scheduling problem of electric vehicles (EVs) at the micro-grid scale. This problem contains a set of charging stations controlled by a central aggregator. One of the main stakeholders is the operator of the charging stations, who is motivated to minimize the cost incurred by the charging stations, while the other major stakeholders are vehicle owners who are mostly interested in user convenience, as they want their EVs to be fully charged as soon as possible. A bi-objective optimization problem is formulated to jointly optimize two factors that correspond to these stakeholders. An online centralized scheduling algorithm is proposed and proven to provide a Pareto-optimal solution. Moreover, a novel low-complexity distributed algorithm is proposed to reduce both the transmission data rate and the computation complexity in the system. The algorithms are evaluated through simulation, and results reveal that the charging time in the proposed method is 30% less than that of the compared methods proposed in the literature. The data transmitted by the distributed algorithm is 33:25% lower than that of a centralized one. While the performance difference between the centralized and distributed algorithms is only 2%, the computation time shows a significant reduction. 11 pages. accepted by IEEE Transactions on Smart Grid

In this paper, we consider the optimal meeting scheduling problem in a commercial building over a fixed period of time, with the objectives of minimizing the cost of energy consumption by the air-conditioning system and possibly achieving more balanced power distribution. By considering a set of realistic factors, including the eligible time slots of attendees and energy consumption characteristics of meeting rooms, this problem is formulated as a constrained mixed-integer linear program, which then can be solved by an optimization solver, e.g., CPLEX. However, because the computation complexity increases dramatically with the problem size, a fast heuristic algorithm is proposed. The numerical simulations verify that the heuristic algorithm produces a near-optimal result.