Donato Zarrilli

Optimal Allocation of Energy Storage Systems for Voltage Control in LV Distribution Networks

This paper addresses the problem of finding the optimal configuration (number, locations, and sizes) of energy storage systems (ESSs) in a radial low voltage distribution network with the aim of preventing over- and undervoltages. A heuristic strategy based on voltage sensitivity analysis is proposed to select the most effective locations in the network where to install a given number of ESSs, while circumventing the combinatorial nature of the problem. For fixed ESS locations, the multi-period optimal power flow framework is adopted to formulate the sizing problem, for whose solution convex relaxations based on semidefinite programming are exploited. Uncertainties in the storage sizing decision problem due to stochastic generation and demand, are accounted for carrying out the optimal sizing over different realizations of the demand and generation profiles, and then taking a worst-case approach to select the ESS sizes. The final choice of the most suitable ESS configuration is done by minimizing a total cost, which takes into account the number of storage devices, their total installed capacity and average network losses. The proposed algorithm is extensively tested on 200 randomly generated radial networks, and successfully applied to a real Italian low voltage network and a modified version of the IEEE 34-bus test feeder.

Bidding Wind Energy Exploiting Wind Speed Forecasts

In this paper, we address the problem of determining the optimal day-ahead generation profile for a wind power producer by exploiting wind speed forecasts provided by a meteorological service. In the considered framework, the wind power producer is called to take part in the responsibility of system operation by providing day-ahead generation profiles and undergoing penalties in case of deviations from the schedule. Penalties are applied only if the delivered hourly energy deviates from the schedule more than a given relative tolerance. The optimal solution is obtained analytically by formulating and solving a stochastic optimization problem aiming at maximizing the expected profit. The proposed approach consists in exploiting wind speed forecasts to classify the next day into one of several predetermined classes, and then selecting the optimal solution derived for each class. The performance of the bidding strategy is demonstrated using real data from an Italian wind plant and weather forecasts provided by a commercial meteorological service.

Bidding strategies for renewable energy generation with non stationary statistics

Abstract The intrinsic variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity grid. This paper studies the problem of optimizing energy bids for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial penalties for generation shortfall and surplus. To this purpose, an optimization procedure is devised to cope with the intermittent nature of PV generation and maximize the expected profit of the producer. Since the optimal offer turns out to be a suitable percentile of the PV power cumulative distribution function (cdf), we investigate two approaches to properly take into account the effects of seasonal variation and non stationary nature of PV power generation in the estimation of PV power statistics. The first one normalizes the generated power with the power obtainable under clear-sky conditions. The second approach estimates a time-varying PV power cdf using only power data in a moving window of suitable width. A numerical comparison of the different bidding strategies is performed on a real data set from an Italian PV plant.

Algorithms for placement and sizing of energy storage systems in low voltage networks

This paper addresses the problem of optimal placement and sizing of distributed energy storage devices in a low voltage network. The objective is to find the configuration which minimizes the total cost of storage devices, which depends both on the number of storage devices and on their size. The optimal power flow framework is adopted for formulating the overall optimization problem. Since the exact problem turns out to be intractable in realistic applications, we adopt a semidefinite programming relaxation for the power flow constraints and different heuristics for circumventing the combinatorial problem of selecting the most appropriate buses where to allocate the storage devices. The overall procedure is tested on a real application involving a portion of an Italian low voltage network.

Receding horizon control for demand-response operation of building heating systems

In this paper we consider the problem of optimizing the operation of a building heating system under the hypothesis that the building is included as a consumer in a Demand Response program. Demand response requests to the building are assumed to come from an external market or grid operator. The requests assume the form of price/volume signals specifying a volume of energy to be saved during a given time slot and a monetary reward assigned to the participant in case it fulfills the conditions. A receding horizon control approach is adopted for minimization of the energy bill, by exploiting a simplified model of the building. Since the resulting optimization problem is a mixed integer linear programming problem which turns out to be manageable only for buildings with very few zones, a heuristic is devised to make the algorithm applicable to realistic size problems as well. The derived control law is tested on the realistic simulator EnergyPlus to evaluate pros and cons of the proposed algorithm. The performance of the suboptimal control law is evaluated by comparison with the optimal one on a chosen test case.

Wind power bidding in a soft penalty market

In this paper we consider the problem of offering wind power in a market featuring soft penalties, i.e. penalties are applied whenever the delivered power deviates from the nominal bid more than a given relative tolerance. The optimal bidding strategy, based on the knowledge of the prior wind power statistics, is derived analytically by maximizing the expected profit of the wind power producer. Moreover, the paper investigates the use of additional knowledge, represented by wind speed forecasts provided by a meteorological service, to make more reliable bids. The proposed approach consists in exploiting wind speed forecasts to classify the day of the bidding into one of several predetermined classes. Then, the bids are represented by the optimal contracts computed for the selected class. The performance of the optimal bidding strategy, both with and without classification, is demonstrated on experimental data from a real Italian wind farm, and compared with that of the naive bidding strategy based on offering wind power forecasts computed by plugging the wind speed forecasts into the wind plant power curve.

Exploiting weather forecasts for sizing photovoltaic energy bids

In this work, we study the problem of optimizing energy bids for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial penalties for generation shortfall and surplus. The optimal bidding strategy depends on the statistics of the PV power generation and on the monetary penalties applied. We show how to tune the bidding strategy on the basis of the weather forecasts. To this purpose, an optimization procedure is devised to mitigate the risk associated with the intermittent nature of PV generation and maximize the expected profit of the producer. We also investigate an approach to properly take into account the seasonal variation and non stationary nature of PV power generation statistics, by exploiting the knowledge of the amount of energy that the plant can generate under clear-sky conditions. The proposed bidding strategy is validated on a real data set from an Italian PV plant.

Energy Storage Operation for Voltage Control in Distribution Networks: A Receding Horizon Approach

The widespread diffusion of renewable energy sources and low carbon technologies in distribution electricity grids calls for counteracting overvoltage and undervoltage arising in low voltage (LV) feeders, where peaks of load demand and distributed generation are typically not aligned in time. In this context, deployment of energy storage systems (ESSs) in appropriately selected nodes of the network is recognized as a viable approach to tackle the problem. This paper proposes a voltage control scheme based on a receding horizon approach to operate the ESSs installed in an LV network. The essential feature of the approach lies in the very limited information needed to predict possible voltage problems, and to compute the storage control policy making it possible to counteract them in advance. The procedure is successfully applied to an Italian LV network, featuring demand and generation profiles, which cause overvoltage and undervoltage in the absence of voltage control.

Energy storage sizing for voltage control in LV networks under uncertainty on PV generation

Energy storage systems may represent a viable solution to tackle over- and undervoltages arising in low voltage networks due to the increasing penetration of low carbon technologies. An algorithm for siting and sizing energy storage systems in radial low voltage networks was proposed in previous work. Siting exploits the voltage sensitivity matrix of the network, while sizing is performed by solving a multi-period optimal power flow problem. In this paper, we discuss the sizing step of the aforementioned algorithm for a low voltage network featuring overvoltages due to the high penetration of photovoltaic generation. Since the considered decision problem is affected by uncertainty on photovoltaic generation, a scenario-based approach, coupled with suitable scenario reduction techniques, is analyzed. While this approach is useful to keep the computational burden affordable, we investigate whether the energy storage system sizes found after scenario reduction can guarantee the solution of overvoltages with a priori defined confidence level. This is done by testing the overall procedure on a real Italian low voltage network provided by the main Italian distribution system operator.

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts.

In recent years, the interest in clean renewable energy resources, such as wind and photovoltaic, has grown rapidly. It is well known that the inherent variability in wind power generation and the related difficulty in predicting future generation profiles, raise major challenges to wind power integration into the electricity grid. In this work we study the problem of optimizing energy bids for an independent Wind Power Producer (WPP) taking part into a competitive electricity market. It is assumed that the WPP is subject to financial penalties for generation shortfall and surplus. This means that, if the energy delivered over a given time slot is different from that subscribed in the bid, the WPP will be penalized, the monetary entity of the penalty depending on the wholesale market behavior, the day of the year and the time slot involved. An optimization procedure is devised to minimize this risk and maximize the expected profit of the seller. Specifically, each energy bid is computed by exploiting the forecast energy price for the day ahead market, the historical wind statistics at the plant site and the day-ahead wind speed forecasts provided by a meteorological service. We also examine and quantify the strategic role of an energy storage device in increasing reliability of bids and mitigating the financial risks of the WPP.