Abstract
Electric springs (ESs) have proven effective for integrating renewable generation into power systems. An ES connected in series with a non-critical load forms a smart load whose consumption can be dynamically controlled for voltage regulation and demand side management. In most existing applications, smart loads have been devoted to providing services to the grid without accounting for their own interests. The novelty of this paper is to propose a price-based strategy to coordinate the operation of multiple ESs in microgrids. Smart loads consisting of ESs connected to electric water heaters are modeled as rational agents that locally optimize their own objectives by adjusting their consumption schedules in response to price/control signals. Such signals are determined at the microgrid central controller (MGCC) when solving the microgrid operation scheduling problem formulated to minimize the microgrid operation cost taking into account the smart loads' consumption schedules. An iterative optimization algorithm determines the equilibrium between the microgrid and smart loads' objectives requiring only the exchange of price/control signals and power schedules between the local controllers and the MGCC. Case studies show the effectiveness of the proposed strategy to economically benefit both the microgrid and smart loads when scheduling their operation.

Abstract
Yearly, the number of Distributed Energy Resources (DER) integrated into the power grid increases has increased, having a large impact on power generation globally, promoting the introduction of renewable energy resources (RER). To increase the flexibility of the power system with integrated RER, the introduction of energy storage systems (ESS) is essential. Demand response (DR) programs also help to increase grid flexibility, resulting in increased grid reliability as grid congestion and losses decrease. However, this new paradigm shift needs further research and careful analysis. In this work, two types of DR programs are addressed to promote greater participation by different consumers features. To interconnect the different consumers, DR aggregators are inserted to ensure that individual consumers have influence on the power market. All these aspects, if accompanied by information, measurement, communication, and control systems, give rise to the smart grids, playing an essential role. The results show, considering the worst uncertainty case scenario, that there is a suitable total RER of 2151.50 kW, against 3227.30 kW, by not considering the RER uncertainty.

Abstract
The optimal management of distributed energy resources (DERs) and renewable-based generation in multi -energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy sys-tems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic pro-gramming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk -seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The pro-posed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.

Abstract
In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

Abstract
Energy storage is an important element of an energy system. In the power system, energy storage can be defined as a component that can be employed to generate a form of energy or utilize previously stored energy at different locations or times when it is required. Energy storage can enhance the stability of the grid, increase the reliability and efficiency of integrated systems that include renewable energy resources, and can also reduce emissions. A diverse set of storage technologies are currently utilized for the energy storage systems (ESSs) in a varied set of projects. This chapter provides information about the current ESS projects around the world and emphasizes the leading countries that are developing the applications of ESSs. The main categories of ESSs are explained in this chapter as follows: electrochemical, electromechanical, electromagnetic, and thermal storage. Moreover, the energy storage technologies are utilized in power grids for various reasons such as electricity supply capacity, electric energy time-shifting, on-site power, electric supply reserve capacity, frequency regulation, voltage support, and electricity bill management. Additionally, by integrating the various energy forms and developing the concept of multi-energy systems, ESSs become a fundamental component for the efficient operation of multi-energy systems. The main role of ESSs in multi-energy systems is to compensate for the fluctuations in power output from renewable energy resources. Moreover, the performance of the multi-energy system increases when it got integrated with an ESS. In this chapter, the applied ESS technologies in the context of the multi-energy systems are presented and explained.

Abstract
As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as shoeboxes using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.