Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

Temporal flexibility options in electricity market simulation models: Deliverable D4.1

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: This report covers the implementation of temporal flexibility options in TradeRES’ agent-based electricity market simulations models. Within this project, the term “temporal flexibility option” was defined as an asset or measure supporting the power system to balance electric demand and supply and compensate for their stochastic fluctuations stemming from, e.g., weather or consumer behaviour by adjusting demand and/or supply as a function over time or by reducing their forecast uncertainty. Other reports from the same work package of TradeRES are published almost simultaneously, each focussing on another aspect of market model enhancements. These accompanying reports address sectoral flexibility, spatial flexibility, actor types, and modelling requirements for market designs. Flexibility options covered in this report were selected with regard to a predominantly temporal characteristic, a contribution to TradeRES’ assessment of market designs, and the feasibility to be implemented in at least one of the agent based models (ABM) during the project’s lifetime. The technical aspects of “Load shedding”, “Load shifting”, “Electricity storage”, and “Real-time pricing” were selected for implementation. In addition, the following new electricity market products were selected for implementation: “Rolling market clearing”, “Trading with shorter time units”, and “Variable market closure lead times”.N/

A Three-Step Methodology to Improve Domestic Energy Efficiency

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u

OPTIMIZING THE USE OF ENERGY STORAGE AS A DEMAND RESPONSE TOOL

The renewable energies expansion over last years, due to the need to bring electricity production towards ever higher levels of green production and the increase of the demand, have brought further stability problems to the main grid. The handling of the integration of these alternative sources and the optimization of the electricity grid have given high attention on the role of demand response program as a key part for the target. The combination of battery storage units with real-time prices is part of the research effort that aims to reduce the instability of the grid and the energy costs of the users. Literature shows good potential for the control strategies as the relative wide range of technologies developed recently for the scope, even if for the residential customers usually the potential is constrained by the limited controllable loads and their significant share of consumption. However, the aspect of user comfort is not always fully considered leading to less realistic conclusions. The objective of the work described in the dissertation was then to obtain a reduction in residential energy costs through the optimal scheduling of user appliances supported by the use of battery storage, under a real-time price scheme, while limiting the discomfort for the customer. Although the first results of applying a real time pricing scheme based on the current variations in price observed in the Iberian wholesale market led only to small profits when not considering additional self-generation, they increased significantly if a small photovoltaic based production is considered, and reached significant cost savings (circa 70%) in periods of high solar generation. But, when applying a real time price following the fluctuations of the renewable energy supply, which produced much higher variations in price, the results improved considerably, reaching cost savings as high as 85%. The implemented model shows the true relevance of Demand Response and Energy Storage, producing meaningful savings if the supply costs change with the availability of renewable energy supply. With self-generation, the obtained value is even higher in the perspective of the individual customer, maximizing the cost-effectiveness of such investment

Dynamic electricity pricing for electric vehicles using stochastic programming

Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs’ demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers’ satisfaction in addition to improve the profitability of the energy aggregation business.info:eu-repo/semantics/acceptedVersio

Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally