Wind-pv-thermal power aggregator in electricity market

This paper addresses the aggregation of wind, photovoltaic and thermal units with the aim to improve bidding in an electricity market. Market prices, wind and photovoltaic powers are assumed as data given by a set of scenarios. Thermal unit modeling includes start-up costs, variables costs and bounds due to constraints of technical operation, such as: ramp up/down limits and minimum up/down time limits. The modeling is carried out in order to develop a mathematical programming problem based in a stochastic programming approach formulated as a mixed integer linear programming problem. A case study comparison between disaggregated and aggregated bids for the electricity market of the Iberian Peninsula is presented to reveal the advantage of the aggregation

Wireless battery charger for ev with circular or planar coils: comparison

This paper presents the experimental results obtained in the wireless energy transfer system prototype based on circular or planar coils. With these experimental results we can choose the tuning settings to improve the power transmission efficiency in wireless energy transfer systems. In wireless energy transfer for electric vehicle batteries charging, the coil shape and the range between the coils are the most important issues of those systems

Self-scheduling of wind-thermal systems using a stochastic MILP approach

In this work a stochastic (Stoc) mixed-integer linear programming (MILP) approach for the coordinated trading of a price-taker thermal (Ther) and wind power (WP) producer taking part in a day-ahead market (DAM) electricity market (EMar) is presented. Uncertainty (Uncer) on electricity price (EPr) and WP is considered through established scenarios. Thermal units (TU) are modelled by variable costs, start-up (ST-UP) technical operating constraints and costs, such as: forbidden operating zones, minimum (Min) up/down time limits and ramp up/down limits. The goal is to obtain the optimal bidding strategy (OBS) and the maximization of profit (MPro). The wind-Ther coordinated configuration (CoConf) is modelled and compared with the unCoConf. The CoConf and unCoConf are compared and relevant conclusions are drawn from a case study

Modeling and Simulation of PV Panel Under Different Internal and Environmental Conditions with Non-constant Load

This paper focuses on PV power conversion under different internal and environmental conditions with non-constant load, connected to a smart grid system. Due to environmental conditions, the PV system is a non-linear system and difficult to predict the power conversion. In the aspect of internal variables, it includes the five parameters of the single diode solar cell model identify their sensitivity through error function. It also identifies the relation between environmental conditions, mainly: irradiance, temperature and wind speed. The modeling and computational simulation with laboratory work identify the effects of internal and environmental effect on the system. The model gives details about the sensitivity of each environmental condition using error function. The work includes the decrease of energy conversion by the solar panel as a function of time due to the shadow effect that affects its performance. Besides these, a smart system is introduced as a DAQ system in laboratory environment to get in real time the power conversion value with the P-V and I-V characteristics of the PV panel

Scenario Reduction for Stochastic Optimization Applied to Short-term Trading of PV Power

This paper addresses the scenario reduction for stochastic optimization applied to short-term trading of photovoltaic (PV) power. Stochastic optimization becomes a useful technique when leading with problems involving uncertainty. Short-term trading of PV power in electricity markets is an example of a problem involving a high level of uncertainty, namely uncertain parameters as PV power and market prices. As the level of uncertainty grows and the optimization problem becomes more complex, the need to reduce the number of scenarios becomes crucial without losing the representativeness of the original scenarios. Thus, in this paper is proposed an efficient scenario reduction algorithm based on backward method in order to obtain a profitable trading of PV power in electricity markets. The scenario reduction method is applied to a two-period scenario tree, i.e., a scenario fan including uncertainty on day-ahead market prices, on imbalance prices and on PV power. Through a case study is analyzed the performance of the scenario reduction algorithm and the comparison with the original set of scenarios. The results show that the reduced set of scenarios still has a very high level of accuracy

Electric Vehicles Aggregation in Market Environment: A Stochastic Grid-to-Vehicle and Vehicle-to-Grid Management

Part 10: Energy MarketsInternational audienceThis paper addresses a development of a support management system for a power system aggregator managing a fleet of electric vehicles and bidding in a day-ahead electricity market. The support management system is modeled by stochastic mixed integer linear programming approach. The charge and discharge of the batteries of the fleet of vehicles are brought about to a convenient contribution for the maximization of the expected profit of the aggregator. The optimization takes into consideration the profiles of usage of the vehicle owners and the battery degradation of the vehicles. The vehicles are assumed as bidirectional energy flow units: allowing grid-to-vehicle or vehicle-to-grid operation modes. A strong interaction of information exchange is assumed between the aggregator and vehicle owners. A set of scenarios is created by a scenario generation method based on the Kernel Density Estimation technique and are subjected to a reduction by a K-means clustering technique. A case study with data of Electricity Market of Iberian Peninsula is presented to drive conclusion about the support management system developed