Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup

International audienceEnergy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long timescale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty

Load Flexibility for Price based Demand Response

We propose a framework to utilize load flexibility to be operated in a window of flexibility considering the price variations. The consumer inputs the window of flexibility, the period of operation and nominal power consumption trajectory governed by the load. We create a load shift matrix and minimize the cost of consumption of operating the device. For some devices such as electric vehicle, the nominal power consumption trajectory can be altered provided the total energy consumed in the window of flexibility is matched. The new power consumption trajectory can be found using profile steering. We also show that under the price taker assumption, the optimal control of aggregate of flexible loads is equivalent to optimally controlling each of the loads individually. Using real data from Pecan Street [1] and ERCOT wholesale market price [2] we conduct numerical experiment showing the efficacy of the proposed mechanism of performing price based demand response (DR)

Arbitrage with Power Factor Correction using Energy Storage

The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries.Comment: 10 pages, 8 figure

Limiting Energy Storage Cycles of Operation

International audienceBatteries are an expensive form of energy storage, therefore, must be operated in an efficient manner. Battery life is often described a combination of cycle life and calendar life. In this work we propose a mechanism to limit the number of cycles of operation over a time horizon in a computationally efficient manner. We propose a modification in an optimal arbitrage algorithm proposed in our previous work [1] to efficiently control the number of cycles of operation of a battery. The cycles of operation have to be tuned based on price volatility to maximize the battery life and arbitrage gains. We propose a mechanism to distinguish arbitrage returns

Leadership behavior : structural relationship of emotional intelligence (self awareness, self management, social awareness and relationship management) among public school personnel in Malaysia

Various changes in the educational system have evolved for educator leaders recently. However, negative impacts on leader’s emotional intelligence due to leadership behavior that arise problems in producing good quality of work can have significant effect on their performance. This study investigates the impact of an emotional intelligence on leadership behaviour aswell as fitness of the suggested hypothetical model and the observed data. Using a structured questionnaire derived from the literature, data were collected from 306 (89%) subjects from a 15 SBT (High Performance Schools) in Malaysia which were chosen by cluster sampling procedur. the data were then analyzed using structural equation modeling (SEM); an acceptable level of model fit was found. Two sets of questionnaire to measure emotional intelligence (Emotional Competence Inventory) (ECI) and leadership behavior (Multi-factor leadership questionnaires (MLQ) were administered. The inclusion criteria consisted of leaders in a school setting. Results: Three and-six educator leaders from fifteen SBT schools in Malaysia were recruited into the study. Respondents were on average 41 years old (range= 26-60 years). The Majority were female (n=174), education level (degree; n=255), and working experience (range=11-20 years; n=107. In examining the relationship between emotional intelligence with leadership behavior, EQ was positively related to transformational leadership, p < 0.001, R2 = 0.61, and transactional leadership, p< 0.001, R2 = 0.77. The result shows that the model fit indices demonstrates TLI (Tucker Index), NFI (Normed Fit), RFI (Relative Fit Index), IFI (Incremental Fit Index) and CFI (Comparative Fit Index) are more than .90 (from .932 until .975), RMSEA (Root Mean Square Error of Approximation) also shows 0.055 (RMSEA <. 08) (Byrne, 2001). These values indicate an adequate fit and it’s obviously met the basic requirment of model fit. The values of the Chi-Squared Roots Goodness of Fit also show 1.87, below 3. Therefore, the model is significantly fit and support to the data. On a practical note, the assessment of psychological constructs in school setting eg; EQ and leadership could possibly assist in enhancing the work performances in delivering huge benefits to the society especially in the educational contexts