This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.
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Here, we delve into Demand Forecasting via Machine Learning, dissecting how to predict future demand using time-sensitive data. Westveer highlights key forecasting models, from the basic Simple Exponential Smoothing to the advanced SARIMA, applied to an electricity production dataset. The session, encapsulating the essence of data-driven forecasting, culminates in a compelling three-year predictive outlook, illustrating the transformative potential of machine learning in strategic planning and decision-making.
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One of the issues concerning the replacement of natural gas by green gas is the seasonal pattern of the gas demand. When constant production is assumed, this may limit the injected quantity of green gas into a gas grid to the level of the minimum gas demand in summer. A procedure was proposed to increase thegas demand coverage in a geographical region, i.e., the extent to which natural gas demand is replaced by green gas. This was done by modeling flexibility into farm-scale green gas supply chains. The procedure comprises two steps. In the first step, the types and number of green gas production units are determined,based on a desired gas demand coverage. The production types comprise time-varying biogas production, non-continuous biogas production (only in winter periods with each digester having a specified production time) and constant production including seasonal gas storage. In the second step locations of production units and injection stations are calculated, using mixed integer linear programming with cost price minimization being the objective. Five scenarios were defined with increasing gas demand coverage, representing a possible future development in natural gas replacement. The results show that production locations differ for each scenario, but are connected to a selection of injection stations, at least in the considered geographical region under the assumed preconditions. The cost price is mainly determined by the type of digesters needed. Increasing gas demand coverage does not necessarily mean a much higher cost price.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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Food security depends on a network of actors and elements working together to produce and deliver healthy, sustainable, varied, safe and plentiful food supply to society. The interactions between these actors and elements must be designed, managed and optimized to satisfy demand. In this chapter we introduce Food Supply Chain Optimization and Demand, providing a framework to understand and improve food security from an operational and strategic point of view.
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The possibilities of balancing gas supply and demand with a green gas supply chain were analyzed. The considered supply chain is based on co-digestion of cow manure and maize, the produced biogas is upgraded to (Dutch) natural gas standards. The applicability of modeling yearly gas demand data in a geographical region by Fourier analysis was investigated. For a sine shape gas demand, three scenarios were further investigated: varying biogas production in time, adding gas storage to a supply chain, and adding a second digester to the supply chain which is assumed to be switched off during the summer months. A regional gas demand modeled by a sine function is reasonable for household type of users as well as for business areas, or a mixture of those. Of the considered scenarios, gas storage is by far the most expensive. When gas demand has to be met by a green gas supply chain, flexible biogas production is an interesting option. Further research in this direction might open interesting pathways to sustainable gas supply chains.
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It is a challenge for mathematics teachers to provide activities for their students at a high level of cognitive demand. In this article, we explore the possibilities that history of mathematics has to offer to meet this challenge. History of mathematics can be applied in mathematics education in different ways. We offer a framework for describing the appearances of history of mathematics in curriculum materials. This framework consists of four formats that are entitled speck, stamp, snippet, and story. Characteristic properties are named for each format, in terms of size, content, location, and function. The formats are related to four ascending levels of cognitive demand. We describe how these formats, together with design principles that are also derived from the history of mathematics, can be used to raise the cognitive level of existing tasks and design new tasks. The combination of formats, cognitive demand levels, and design principles is called the 4S-model. Finally, we advocate that this 4S-model can play a role in mathematics teacher training to enable prospective teachers to reach higher cognitive levels in their mathematics classrooms.
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In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model and sociodemographic statistics of neighborhoods.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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