Johann Leithon

Online energy management strategies for base stations powered by the smart grid

We consider the problem of minimizing the electricity bill for a cellular base station powered by the smart grid and locally harvested renewable energy. We consider hourly-varying electricity prices made known one day ahead to the base station. We assume that the base station is equipped with a finite-capacity battery. We ensure that the instantaneous energy demand of the base station is satisfied and the constraints imposed by the battery are observed at any point in time. We propose several online energy management strategies that require only causal knowledge of the renewable energy generation and the power consumption profiles. We benchmark our proposed strategies against the optimal energy management policy which assumes perfect knowledge of all system parameters, e.g., base station energy usage and renewable energy generation, both in the past and the future. Simulation results show that the performance of our proposed online strategy deviates from the optimal by 2% at most.

New uplink opportunistic interference alignment: An active alignment approach

In this paper, we propose two new opportunistic interference alignment (IA) schemes to mitigate interference in the K-cell uplink interference channel. Unlike the existing schemes that basically rely on the channel randomness to achieve asymptotical IA for all cells, the proposed schemes employ an active alignment approach to increase the possibility for perfect partial IA for one cell within the network. Specifically, each user adopts the active alignment transmit beamforming such that the user-generated interference is perfectly aligned along the preferred reference interference direction at one of the base-stations (BS) in other cells. With this approach, our schemes increase the chance to obtain degrees-of-freedom gain even with a small number of users. In addition, several new user selection schemes are proposed, and the BS receiver is optimized to enhance performance. Extensive simulations are conducted, and the results show that the proposed schemes outperform the state-of-the-art under the considered scenarios.

A new opportunistic interference alignment scheme and performance comparison of MIMO interference alignment with limited feedback

We investigate the performance of several interference alignment (IA) schemes for a three-user MIMO channel with limited feedback. In our study, we consider traditional IA with quantized channel state information at each transmitter, and opportunistic IA (OIA) that exploits the idea of opportunistic selection to select users whose interfering channels are most aligned. The performance of the former is determined by the quantization resolution, whereas the performance of the later depends on the number of users available for selection. We show that OIA only requires a small number of users to achieve a comparable performance to traditional IA schemes with quantized feedback. Motivated by this fact, we propose a new user selection criterion that outperforms the existing state of the art. Simulations show the superior performance of our OIA scheme under different strong/mild interference conditions.

Energy exchange among base stations in a Cellular Network through the Smart Grid

In this paper, we study the problem of minimizing the energy cost incurred by a Cellular Network Operator (CNO) in a Smart Grid (SG) environment. We consider a CNO that deploys several Cellular Base Stations (CBS) to serve a given geographical area. Each CBS is equipped with a limited-capacity battery and can be powered either by the SG or by a renewable-energy (RE) harvester. Given this topology, two-way energy flow is allowed between each CBS and the SG and between any pair of CBSs in the network through the SG. The space-time-dependent energy-buying and energy-sharing costs and the energy-selling prices are made known to the CNO in advance. Therefore, in order to minimize the total cost incurred by the CNO, we find the optimal energy-management strategy by solving a constrained optimization problem. The proposed strategy ensures that the instantaneous energy demand of each CBS and the constraints imposed by each battery are satisfied at every point in time. We evaluate the performance of the proposed solution using simulations. Our results show that a significant cost reduction can be achieved by implementing the proposed strategy.

Relay Selection Algorithms for Analog Network Coding OFDM Systems

We propose two relay selection schemes for analog network coding (ANC) OFDM systems. One uses OFDM Symbol-Based Max-Min-Min Selection Criterion (OSBM3SC) to select only one relay for signal transmission, and the other uses a Subcarrier-Based Max-Min Selection Criterion (SBM2SC) to select multiple relays for signal transmission. Assuming a frequency-domain fully-uncorrelated Rayleigh fading channel, we derive the asymptotic symbol error rate (SER) performance for both schemes, and show that they both can achieve full diversity. Using extensive simulations, we further show that the presented analytical results are useful to accurately predict the high SNR error performance of practical OFDM systems.

Energy management strategies for base stations powered by the smart grid

In this paper we study the problem of energy management in cellular base stations powered by smart grids and renewable energy. The utility company, through the smart grid, offers hourly-varying electricity prices made known a day ahead to the base station. We formulate an optimization problem in which the cost function is defined as the billing cost of the energy consumed each day. We seek to minimize the cost function while meeting the energy demand of the base station. We assume that the base station is equipped with a finite-capacity battery and a renewable source of energy such as a solar panel. The battery incurs charging and discharging losses which are accounted in the problem formulation. We find the optimal energy management policy using linear programming techniques. Furthermore, we study how the optimal cost is affected by several system parameters such as: initial state of the battery, its capacity, maximum charging/discharging rates, losses, smoothness of the price profile and correlation between the price and the consumption profiles. Our results show that significant cost savings can be achieved by properly scheduling the battery.

Battery-Aided Demand Response Strategy Under Continuous-Time Block Pricing

In this paper, we propose a battery-aided demand response strategy to minimize the energy expenditure incurred by grid-tied systems such as domestic and industrial loads. Unlike existing algorithms in the literature, our proposed strategy takes into account the non-linear behavior of the rechargeable battery. In addition, continuous-time block tariffing is adopted as a pricing strategy that generalizes current schemes such as time-of-use and consumption-based block pricing. To find the optimal demand response strategy, we formulate a nonconvex optimization problem, for which we obtain an approximate solution by introducing linearization and discretization in time. To gain further insight, we derive an analytical solution by introducing some simplifying approximations that allow us to use calculus of variations to obtain closed-form analytical results for the optimal charging and discharging schedules. Through simulations, we show that the strategy based on calculus of variations is able to achieve better performance and incur smaller computational costs than the solution based on discretisation in time. Finally, we discuss how the energy expenditure is related to pricing parameters, and specifications of the battery such as size and Peukert exponent.

Energy management strategies for base stations in a smart grid environment

In this paper, we propose an optimal energy management strategy that minimises the energy bill incurred by cellular base stations (CBSs) in a smart grid environment. The CBS can harvest renewable energy for local use and is equipped with a limited-capacity battery that is subject to charging and discharging losses. Because the proposed optimal strategy requires non-causal information and hence cannot be implemented online, we propose an estimation-based strategy that only requires causal knowledge of the renewable energy generation and the energy consumption at the CBS. Through simulations, we show that the proposed online estimation-based strategy has only a small performance gap from the optimal offline strategy. Additionally, we derive and verify three analytical results: an upper bound for the optimal cost, the critical battery capacity, above which no further cost reduction can be achieved, and the conditions on the energy price profile, which ensure that the savings can outweigh both the operational and the per-cycle battery costs. These analytical results can be used as guidelines in designing energy storage systems for CBSs in a smart grid environment. Copyright

Online Demand Response Strategies for Non-Deferrable Loads With Renewable Energy

We propose two online demand response strategies to minimize the operational expenditure incurred by a non-deferrable load facility over a finite planning horizon. The facility is permanently connected to the grid, and is equipped with a rechargeable battery and a renewable energy (RE) harvester. The rechargeable battery can be operated in a linear or a non-linear regime, the latter being modeled by using Peukert’s Law. The first proposed demand response strategy (DRS) is based on forecasting techniques. We specifically use a time-inhomogeneous Markov chain model to estimate future RE arrivals. The second proposed DRS is based on continuous-time optimal control theory, and does not require explicit estimates of future RE arrivals when the battery operates in its non-linear regime. Existing works in the literature require extensive computations, e.g., solving a linear program on a rolling basis, and ignore the non-linear relationship between the discharging rate and the remaining charge. In contrast, the two proposed DRSs take into account the non-linear behavior of the discharging operation, and the second proposed DRS requires fewer computations than existing solutions.

Renewable energy management in cellular networks: An online strategy based on ARIMA forecasting and a Markov chain model

In this paper, we propose an online energy management strategy to minimize the operational expenses incurred by cellular base stations powered by both renewable and conventional energy. Our proposed strategy uses an Auto Regressive Integrated Moving Average (ARIMA) time series model for inter-day forecasting, a Markov chain model for intra-day predictions, and linear programming techniques for optimizing the decision variables on a real-time basis. To the best of our knowledge, the potential of these techniques has not been sufficiently explored in the literature. We assume that the base station is equipped with a rechargeable battery and a solar panel. Moreover, we consider real-time electricity pricing, and the application of a net-metering policy, whereby consumers are allowed to add excess renewable energy to the grid and obtain kilowatt credits in return. To evaluate the performance of the proposed algorithm, we devise an offline strategy which assumes non-causal knowledge of renewable energy generation and hence provides an upper bound in performance. Finally, we present numerical results obtained using real meteorological data, practical solar panel specifications, and factual energy tariffs. Through simulations we benchmark the proposed algorithm against the genie-aided strategy, and show its robustness by considering random energy rates.