Power Strip Packing of Malleable Demands in Smart Grid

We consider a problem of supplying electricity to a set of $\mathcal{N}$ customers in a smart-grid framework. Each customer requires a certain amount of electrical energy which has to be supplied during the time interval $[0,1]$. We assume that each demand has to be supplied without interruption, with possible duration between $\ell$ and $r$, which are given system parameters ($\ell\le r$). At each moment of time, the power of the grid is the sum of all the consumption rates for the demands being supplied at that moment. Our goal is to find an assignment that minimizes the {\it power peak} - maximal power over $[0,1]$ - while satisfying all the demands. To do this first we find the lower bound of optimal power peak. We show that the problem depends on whether or not the pair $\ell, r$ belongs to a "good" region $\mathcal{G}$. If it does - then an optimal assignment almost perfectly "fills" the rectangle $time \times power = [0,1] \times [0, A]$ with $A$ being the sum of all the energy demands - thus achieving an optimal power peak $A$. Conversely, if $\ell, r$ do not belong to $\mathcal{G}$, we identify the lower bound $\bar{A} >A$ on the optimal value of power peak and introduce a simple linear time algorithm that almost perfectly arranges all the demands in a rectangle $[0, A /\bar{A}] \times [0, \bar{A}]$ and show that it is asymptotically optimal

Privacy-preserving Energy Scheduling for Smart Grid with Renewables

We consider joint demand response and power procurement to optimize the average social welfare of a smart power grid system with renewable sources. The renewable sources such as wind and solar energy are intermittent and fluctuate rapidly. As a consequence, the demand response algorithm needs to be executed in real time to ensure the stability of a smart grid system with renewable sources. We develop a demand response algorithm that converges to the optimal solution with superlinear rates of convergence. In the simulation studies, the proposed algorithm converges roughly thirty time faster than the traditional subgradient algorithm. In addition, it is fully distributed and can be realized either synchronously or in asynchronous manner, which eases practical deployment

Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building

The smart active residential buildings play a vital role to realize intelligent energy systems by harnessing energy flexibility from loads and storage units. This is imperative to integrate higher proportions of variable renewable energy generation and implement economically attractive demand-side participation schemes. The purpose of this paper is to develop an energy management scheme for smart sustainable buildings and analyze its efficacy when subjected to variable generation, energy storage management, and flexible demand control. This work estimate the flexibility range that can be reached utilizing deferrable/controllable energy system units such as heat pump (HP) in combination with on-site renewable energy sources (RESs), namely photovoltaic (PV) panels and wind turbine (WT), and in-house thermal and electric energy storages, namely hot water storage tank (HWST) and electric battery as back up units. A detailed HP model in combination with the storage tank is developed that accounts for thermal comforts and requirements, and defrost mode. Data analytics is applied to generate demand and generation profiles, and a hybrid energy management and a HP control algorithm is developed in this work. This is to integrate all active components of a building within a single complex-set of energy management solution to be able to apply demand response (DR) signals, as well as to execute all necessary computation and evaluation. Different capacity scenarios of the HWST and battery are used to prioritize the maximum use of renewable energy and consumer comfort preferences. A flexibility range of 22.3% is achieved for the scenario with the largest HWST considered without a battery, while 10.1% in the worst-case scenario with the smallest HWST considered and the largest battery. The results show that the active management and scheduling scheme developed to combine and prioritize thermal, electrical and storage units in buildings is essential to be studied to demonstrate the adequacy of sustainable energy buildings

Community energy retail tariffs in Singapore: opportunities for peer-to-peer and time-of-use versus vertically integrated tariffs

The deregulation of Singapore’s retail electricity market in 2018 and the rapid adoption of solar rooftops have led to the emergence of a new type of energy transaction, wherein prosumers require flexible tariffs that reflect their willingness to respond to market price signals as well as new business models. The move toward community energy schemes, where prosumers can trade their surplus electricity locally, and the implications this has for tariff design motivates our study. We propose a portfolio of stylized retail tariffs for different market organizations. Among the proposed configurations are time-of-use (ToU), default vertical and peer-to-peer (P2P) tariffs, the last of which operates through a blockchain platform. In this study, each Singaporean district is balanced as a potential future microgrid. An iterative double-auction mechanism is designed to calculate a distributed P2P tariff, looking to maximize the benefit for stakeholders. This tariff is then cleared and compared with a bespoke retail ToU tariff as well as Singapore’s monopolistic regulated vertical tariff

Optimización de tarifas de la energía eléctrica para una respuesta a la demanda por medio de programación lineal

This paper develops a tariff optimization model for demand response by analyzing all costs generated by each stage for delivery energy to final consumers, prioritizing the recovery of all costs associated with the system, transferring it to customers by rates for demand response system "real time pricing”, ARIMA models are used to estimate the possible demand that the system will have during the next day, with this demand an economic dispatch is made considering the costs for generation, transmission, distribution, commercialization, reliability and losses, optimal rates to be applied to different customers of different zones, is the purpose, these rates will be calculated daily and vary depending on the behavior of customer consumption in previous days and the historical data of the system, encouraging the change in electricity consumption for the benefit of customers and the system.En este documento se desarrolla un modelo de optimización de tarifas, para la respuesta a la demanda analizando los costos generados por cada etapa para la entrega de energía a los consumidores finales, priorizando la recuperación de costos asociados al sistema, transfiriéndolos a los clientes por medio de tarifas con el sistema de respuesta a la demanda “real time pricing”, apoyado en modelos ARIMA, se estimará la posible demanda que tendrá el sistema durante el siguiente día, con esta demanda se realiza un despacho económico considerando los costos por generación, trasmisión, distribución, comercialización, confiabilidad y pérdidas; siendo la finalidad el cálculo de tarifas óptimas a ser aplicadas a diferentes clientes de distintos estratos. Estas tarifas serán calculadas diariamente y varían dependiendo el comportamiento del consumo de los clientes en días anteriores y los datos históricos del sistema, fomentando el cambio en consumo eléctrico para beneficio de los clientes y del sistema

Implementation of wide area protection system (WAPS) for electrical power system smart transmission grids

The planning, operation and control of the power system has been evolving since its inception. These changes are due to the advancement in science and technology, and changes in energy policy and customer demands. The envisioned power system - smart grid (SG) - is expected to have functional and operational capabilities that maximize the reliability, minimize generation deficit, and cost issues in the power system. However, many power systems in the world today still operate traditionally, with one-way communication and one-way power flow. Transitioning to a smart grid influences the protection schemes of the power system, as the smart grid is to leverage distributed energy resources (DERs) using distributed generation (DG) units and allow for bi-directional flow of power and information. Therefore, there is a need for advanced protection schemes. Wide-area protection (WAP) techniques are proposed as one of the solutions to solve the protection challenges in the smart grid due to their reliance on wide-area information instead of local information. This dissertation considered three WAP techniques which are differentiated based on the data used for faulted zone detection: (A) Positive sequence voltage magnitude (PSVM), (B) Gain in momentum (GIM) and (C) Sum of positive and zero sequence currents (SPZSC). The dissertation investigated their performances in terms of accuracy in detecting the faulted zones and the faulted lines, and fault clearing time. The investigation was done using three simulation platforms: MATLAB/Simulink, Real-Time (Software in the Loop (SIL)) and Hardware-in-the-Loop (HIL) implementation using Opal-RT and SEL-351A relay. The results show that, in terms of detecting the faulted zones, all the techniques investigated have 100% accuracy in all the 36 tested fault cases. However, in terms of identifying the faulted line in the faulted zone, the algorithms were not able to detect all the 36 tested cases accurately. In some cases, the adjacent line was detected instead of the actual faulted line. In those scenarios, the detected line and the faulted line present similar characteristics making the algorithms to detect the wrong line. For the faulted line detection accuracy, the algorithm (A) has an accuracy of 86%, (B) has an accuracy of 94% and (C) has an accuracy of 92%. The fault clearing times of the algorithms were similar for both the MATLAB/Simulink and realtime simulation without the actual control hardware which was the SEL-351A relay. When the simulation was done with the control hardware through Hardware-in-the-loop, a communication delay was introduced which increased the fault clearing times. The maximum fault clearing time for the techniques investigated through the HIL simulation are 404 ms, 256 ms, and 150 ms for the techniques (A), (B) and (C) respectively and this variation is due to the different fault detection methods used in the three algorithms. The fault clearing time includes communication between the Opal-RT real-time simulator and SEL-351A relay using RJ45 ethernet cable, these fault clearing times can change if a different communication medium is used. From the performance data presented, it is evident that these algorithms will perform better when used as backup protection since the common timer settings for backup protection schemes range from 1200 ms to 1800 ms, while primary protection is expected to respond almost instantaneously, that is, with no initial time delay

Optimization models and algorithms for demand response in smart grid.

For demand response in smart grid, a utility company wants to minimize total electricity cost and end users want to maximize their own utility. The latter is considered to consist of two parts in this research: electricity cost and convenience/comfort. We first develop a system optimal (SO) model and a user equilibrium (UE) model for the utility company and end users, respectively and compare the difference of the two. We consider users\u27 possible preference on convenience over cost-saving under the real-time pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Numerical results show that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. Second, we found that instead of minimizing total cost, if utility company is regulated to maximize the social welfare, the user equilibrium model can achieve identical optimal solution as the system optimal model. We then design a demand response pricing frame work to accomplish this goal under alternative secondary objectives. We also investigate the non-uniqueness of the user equilibrium solution and prove that there exist alternative user equilibrium solutions. In this case, robust pricing is considered using multi-level optimization for the user equilibrium. Third, we study empirical data from a demand response pilot program in Kentucky in an attempt to understand consumer behavior under demand response and to characterize the thermo dynamics when set point for heat, ventilation and air conditioning (HVAC) is adjusted for demand response. Although sample size is limited, it helps to reveal the great variability in consumers\u27 response to demand response event. Using the real data collected, we consider to minimize the peak demand for a system consisting of smart thermostats, advanced hot water heaters and battery systems for storage. We propose a mixed integer program model as well as a heuristic algorithm for an optimal consumption schedule so that the system peak during a designated period is minimized. Therefore, we propose a consumption scheduling model to optimally control these loads and storage in maximizing efficiency without impacting thermal comfort. The model allows pre-cooling and pre-heating of homes to be performed for variable loads in low-demand times. We propose several future works. First, we introduce the concept of elastic demand to our SO model and UE model. The system problem maximizes net benefit to the energy consumers and the user problem is the usual one of finding equilibrium with elastic demand. The Karush-Kuhn-Tucker (KKT) conditions can be applied to solve the elastic demand problems. We also propose to develop algorithms for multi-level pricing models and further collect and analyze more field data in order to better understand energy users\u27 consumption behavior

Demand-Response in Smart Buildings

This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact

Development of Advanced Controller to Achieve Complete Peak Shifting in Light-Weight Residential Buildings Located in Cold Climate

In the cold northern climate of Canada, building energy consumption for space heating during the winter have caused huge stress on electrical grids, especially during the peak hours. Shifting or shaving the peak demand can avoid additional capital investment required to meet extra peak demand for the electrical suppliers. Consequently, in the recent years, several utility companies adopted time-based rates to encourage the customers to shift their consumption from high demand hours to those with lower demand. In this regard, the two most commonly used time-based rates are time-of-use tariffs and critical peak pricing. Achieving peak shifting can reduce the heating cost under time-based rates for consumers. Overall, peak shaving is benefit not only for the electrical suppliers but also for the consumers. Most Canadian residential houses are equipped with a concrete slab in their basement primarily for structural integrity. Such high thermal mass concrete slab can be exploited for heat storage to shift the peak power consumption. To take benefit of the concrete slabs in the basement, in previous research works, the self-learning control system and the heat extraction system were proposed to achieve peak shifting in the basement and in the other floors of the buildings, respectively. Despite several advantages, the major limitation of these studies is that the developed self-learning control system focused only on peak shifting in the basement, while the heat extraction system concept was investigated separately from the self-learning control system. Accordingly, this study focused on developing an advanced controller, which can efficiently operate both electrically heated floor and heat extraction system with the objective of achieving the peak shifting, heating cost savings and guaranteeing the thermal comfort in the whole building. As a preliminary work of this study, the peak shifting ability and heating cost savings potential of the self-learning control system operated electrically heated floor under two electrical tariffs (i.e. time-of-use tariffs and critical peak pricing) was analyzed using a validated TRNSYS-MATLAB model. Later, the advanced controller was developed for extending the peak shifting from the floor with high thermal mass to that without high thermal mass by the electrically heated floors integrated with the heat extraction system. In this regard, the developed TRNSYS-MATLAB model was integrated with the heat extraction system. Consequently, the peak shifting ability, heating cost savings of the advanced controller was compared with the other commonly used peak shifting control strategies (i.e. constant set point control and rule-based control) and the respective results are presented. At last, a parametric study using Taguchi method was performed to explore the effective parameters that significantly influence the performance of electrically heated floor, heat extraction system in terms of peak shifting ability, thermal comfort and capital cost. For this purpose, three levels were considered for five factors (A) concrete slab thickness, (B) insulation thickness, (C) fan flow rate, (D) indoor air temperature upper limit and (E) floor surface temperature upper limit. Based on the results of the parametric study, overall recommendation to design the optimal electrically heated floors and heat extraction system was provided. Regarding the results, the peak shifting, thermal comfort and heating cost saving are presented for two tariffs (time-of-use tariffs and critical peak pricing) considering the floor with concrete. The simulation results showed that the peak shifting can be achieved at 99.7% in critical peak pricing and 97.6% in time-of-use tariffs, respectively. On the other hand, to extend the peak shifting in the whole building, self-learning control integrated with a fan (heat extraction system) can improve the peak shifting in basement (up to 97%) and second floor (up to 88%). The cost saving can also increase around 35%, which can be proven financially attractive to both supplier and owner. At last, through parametric study, the optimal condition for efficient design and operation of electrically heated floor system and heat extraction system was found to be concrete slab thickness of 152.4 mm, an insulation thickness of 101.6 mm, a fan flow rate of 400 CFM, air indoor upper limit of 24.5 °C and floor surface temperature upper limit of 28 °