Integration of Demand Response and Photovoltaic Resources in Residential Segments

Abstract

The development of renewable sources in residential segments is basic to achieve a sustainable energy scenario in the horizon 2030–2050 because these segments explain around 25% of the final energy consumption. Demand Response and its effective coordination with renewable are additional concerns for residential segments. This paper deals with two problems: the demonstration of cost-effectiveness of renewables in three different scenarios, and the application of the flexibility of demand, performing as energy storage systems, to efficiently manage the generation of renewable sources while improving benefits and avoiding penalties for the customer. A residential customer in Spain has been used as example. The work combines the use of a commercial simulator to obtain photovoltaic generation, the monitoring of customer to obtain demand patterns, and the development of a Physically-Based Model to evaluate the capability of demand to follow self-generation. As a main result, the integration of models (load/generation), neglected in practice in other approaches in the literature, allows customers to improve revenue up to 20% and reach a basic but important knowledge on how they can modify the demand, development of new skills and, in this way, learn how to deal with the characteristics and limitations of both Demand and Generation when a customer becomes a prosumer. This synergy amongst demand and generation physically-based models boosts the possibilities of customers in electricity markets.

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

“…Moreover, winter period has been selected for simulation purposes in the following paragraphs, because demand in winter is higher than in summer and the climate in this Spanish area is more restrictive for PV generation possibilities. [37], and Spain [38] adapted and updated from [39]. To obtain some representative profiles, it seems necessary to evaluate load dynamics, and the service the customer obtains from them.…”

Analysis, Evaluation and Simulation of Railway Diesel-Electric and Hybrid Units as Distributed Energy Resources

“…Authors' research group has previously proposed individual load models for HVAC loads and their envelope for residential dwellings, and the further aggregation by several mechanisms for these kinds of loads in other segments of public power systems [53]. In the literature, these models are called Physically Based Models (PBLM).…”

“…In the literature, these models are called Physically Based Models (PBLM). For residential heating (cooling) devices with and without thermal storage (e.g., ceramic bricks), or for water heaters [53,54]. They involve the development of an equivalent "grey box" model (a lumped RC network, usually called 3R2C, 2R2C, 2R1C or 1R1C depending on the number of lumped RC parameters that have been chosen to model thermal admittances or transmittances of each wall for the overall model-3, 2, or 1 [55]), very close to References [51,52], but simplified in complexity (the order of state space equations).…”

“…It should be taken into account that delays may occur throughout the route (stochastic theory helps in the study of these possibilities and is considered in railway traffic management algorithms [14], and that, in some cases, dynamic braking cannot be employed. The "stochastic" nature of the problem is not new in DR and DER policies and models, some of them proposed by authors [53] and applied for this problem with statistic methodologies explained in Reference [56]. Figure 22a shows this last scenario: Braking is performed through resistive braking (obviously, Altaria diesel train services with S-334 are not considered).…”

Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

“…The stochastic model represents a prosumer optimizing its energy management, including flexible loads: shiftable volume, shiftable profile, curtailable and interruptible loads, assuming no efficiency losses. García-Garre et al [42] present an algorithm to coordinate a prosumer's household solar PV and residential DR. Their results show that self-consumption rate raises 25% and payback lowers 20%, but without considering system effects. In [43] a bidding methodology based on stochastic optimization for aggregators with several prosumers (equipped with lossless DR) is proposed.…”