Real-time Market Research Articles

Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets

A multi-energy microgrid (MEMG) consisting of different forms of distributed generation, e.g., combined heat and power (CHP) units and renewable distributed energy resources (RDERs), is considered as a key technology for accommodating RDERs and for the introduction of multiple forms of energy sources into the electricity market due to the multi-energy complementarity and flexible operation modes. However, the MEMG is subject to source and demand uncertainties which are the primary obstacles to its market participation. The source and demand uncertainties will pose serious challenges to the management of the MEMG and incur the penalty cost to participate in the real-time market. To minimize the operational cost, a multi-stage optimal energy management system (EMS) for participating in the deregulated electricity market considering the cost of market participation and the additional cost (e.g. the purchasing natural gas cost and the depreciation cost of energy storage system) is proposed in this paper. The proposed EMS consists of forecasting stage, day-ahead scheduling stage and real-time dispatch stage. The long short-term memory (LSTM) is adopted for day-ahead data forecasting during the forecasting stage. In the day-ahead scheduling stage, the cost for participating in the day-ahead market (DM) is minimized based on the forecasted data. In the real-time dispatch stage, the cost for participating in the intraday balancing market (IBM) and the additional cost are minimized based on the rolling model predictive control (MPC) method. The proposed method is verified and compared with benchmark solutions. The numerical results demonstrate that the proposed solution can outperform the benchmark solutions and reduce the peak-to-average ratio of the total net-load of multiple MEMGs.

Real-Time Marketing on Facebook Profiles of Museums in Response to the COVID-19 Pandemic

The subject discussed in this article concerns the concept of real-time marketing (RTM)—a specific form of marketing communications focused on current events—which was employed by Polish museums on their Facebook profiles in the face of the ongoing pandemic. Exploring this topic required posing a rele-vant research question: Have Polish museums been implementing any pandemic-themed RTM strategies on their Facebook profiles, and if so, to what extent? The answer was obtained during qualitative re-search consisting of two stages, namely, verification of posts on museum Facebook profiles and deter-mining which of them were RTM activities; and content analysis of these RTM posts with the use of a categorization key. Our analysis has revealed that the pandemic-themed RTM activities of the re-searched museums focused on eight topics, namely, the “stay-at-home” campaign, information about restrictions, museums vs. pandemic, promotion of online presence, ideas for spending free time, au-dience vs. pandemic, joy at museum reopening, and Brand Heroes.

Understanding Convergence Bids During Blackouts: Analytical Results and Real-World Implications

Convergence bidding is a financial instrument that is widely adopted in recent years in two-settlement electricity markets to reduce the price gap between the day-ahead market (DAM) and the real-time market (RTM). This paper, for the first time, investigates the operation and impact of convergence bids (CBs) during <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blackouts</i> . First, the amount of load shedding in the RTM is modeled as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">function</i> of the amount of the cleared CBs in the DAM. The sign of the slope of this function is proposed as a metric to determine if a CB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exacerbates</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heals</i> the power outages. Next, a series of mathematical theorems are developed to obtain and characterize this new metric under different network conditions. It is proved that, when there is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">no</i> congestion in the DAM, the metric is always greater than or equal to zero. When there <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">is</i> congestion in the DAM, the metric can be positive or negative. Using numerical case studies, we show that, not only when there is no congestion, but also most often when there is congestion, the introduced metric is positive. Therefore, supply CBs almost always hurt the system during blackouts while demand CBs almost always help the system. Furthermore, the impact of load shedding on the profit of CBs is also analyzed. It is shown that, load shedding usually creates advantage for supply CBs and disadvantage for demand CBs in terms of their profit. The implications of these results are discussed. We also analyze the real-world market data from the California Independent System Operator (ISO) during the blackouts in August 2020. It is shown that, the decision by the California ISO to suspend CBs during this event matches the mathematical and numerical results that are obtained and discussed in this paper.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2&#x0025; at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

Transactive Coordination of Electric Vehicles With Voltage Control in Distribution Networks

This paper proposes a novel sensitivity-based transactive energy (TE) framework for real-time coordination of electric vehicles (EVs) with voltage control in low-voltage (LV) power distribution network (PDN). The proposed framework employs a combination of economic and control mechanisms that enables EVs to participate in the real-time local energy market (LEM) and mitigates the voltage issues in the PDN through EVs’ charge/discharge scheduling. Prioritizing EV owners’ preferences and concerns, the proposed method can achieve customers’ satisfaction through EV owners’ active participation in the LEM, in which they can bid to the retail market and decide on energy transactions. A new sensitivity-based voltage control scheme is presented considering an incentivized dynamic power curtailment of EV charge in the node of the PDN where a voltage violation is investigated. The proposed model is implemented in the IEEE 55-node LV European test feeder system to investigate its effectiveness in different scenarios and at different EV penetration levels. Simulation results demonstrate the effectiveness of the proposed system in terms of achieving higher EV owners’ satisfaction, and robustness in voltage control.

Distributed Transactive Coordination of Residential Communities Aiming at Fulfilling Households’ Preferences

Transactive energy (TE) provides joint market and control functionality to manage distributed energy resources (DERs) in distribution networks. This work develops a real-time TE management framework that allows residential customers to actively join in the real-time transactive market with fulfilling households’ preferences including comfort, economical energy consumption, and privacy-preserving. In this regard, first, a user-friendly algorithm is developed to calculate the real-time willingness to pay (bid) for electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) units considering customers’ preferences and concerns. Then, to preserve the privacy of households, the centralized market-clearing problem to maximize social welfare is decomposed into several subproblems using the alternating direction method of multipliers (ADMM) approach. Also, closed-form solutions to all subproblems are derived to simplify implementation and mitigate the computational complexity instead of solving optimization subproblems directly. This model is then implemented in a case study with several numbers of smart homes. The numerical results illustrate that the proposed distributed transactive model not only satisfies households’ comfort preferences but also decreases the average charging cost of EV batteries by 40% compared to the uncontrolled charging model. Further, the results show that our proposed model significantly mitigates the computational burden of the transactive market clearing problem compared to the centralized approach and the distributed approach without closed-form solutions.

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $100 million per year for a CAISO-size system).

Integrating Thermal-Electric Flexibility in Smart Buildings using Grey-Box modelling in a MILP tool

In a smart grid setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. However, most literature assume stationary temperature set points for heating and cooling. In this work, we propose a grey-box model to investigate how the energy flexibility of the thermal mass of the building may impact its energy flexibility potential as well as the investment decisions of the energy system within a building, by using an already developed investment decision tool, BUILDing’s OPTimal operation and energy design model (BUILDopt) (Lindberg et al. (2016)). As BUILDopt is a Mixed Integer Programming (MIP/MILP) tool, the flexibility models must be linear as well. We evaluate the energy flexibility potential, here called comfort flexibility, for use cases reflecting different heating systems (electric panel ovens vs. ground source heat pump) and operation (flexible vs. non-flexible). The case study of an Office building is performed, which considers electric specific demand, domestic hot water demand and space heating demand. Real historical data for weather and energy prices from Oslo are used, including grid tariffs related energy and monthly peak power. Most of the savings are obtained through peak load reduction, which can reach up to 13-16%. These and the savings from shifting demand away from peak prices lead to total savings of around 2%. Yet, these actions do not require additional investment in heat supply or storage components, nor in building renovations: only system measurement and control components are needed.

Energy trading efficiency in ERCOT’s day-ahead and real-time electricity markets

The efficient market hypothesis (EMH) is a fundamental tenet of active and unfettered market trading. The EMH for intertemporal trading of a commodity such as natural gas implies that today’s futures price is an unbiased predictor of a future delivery period’s spot price and that the difference between today’s futures and spot prices reflects the commodity’s cost of carrying. The EMH for inter-regional trading requires the commodity’s regional price difference to equal the inter-regional transportation cost. Using regional hourly data for January 1, 2011 to December 31, 2020 from the Electric Reliability Council of Texas (ERCOT), we investigate whether the EMH is empirically valid in all ERCOT’s wholesale electricity markets and, if not, the extent of ERCOT’s energy trading inefficiency and what can be done to reduce it. By estimating a parsimonious system of eight price-level regressions and four price-difference regressions, we reject the interday EMH for all regions and the inter-regional EMH for all regional market pairs. However, ERCOT’s extent of trading inefficiency is mild when compared to the wholesale energy prices. Our empirical examples have two policy implications: enhancing ERCOT’s interday trading efficiency entails improving the day-ahead forecasts for solar and wind generation and refining ERCOT’s indicative real-time market prices, while enhancing ERCOT’s inter-regional trading efficiency requires transmission capacity expansion to reduce transmission congestion and line losses.

An Exact Sequential Linear Programming Algorithm for the Optimal Power Flow Problem

Despite major advancements in nonlinear programming (NLP) and convex relaxations, most system operators around the world still predominantly use some form of linear programming (LP) approximation of the AC power flow equations. This is largely due to LP technology's superior reliability and computational efficiency, especially in real-time market applications, security-constrained applications, and extensions involving integer variables, in addition to its ability to readily generate locational marginal prices (LMP) for market applications. In the aim of leveraging the advantages of LP while retaining the accuracy of NLP interior-point methods (IPMs), this paper proposes a sequential linear programming (SLP) approach consisting of a sequence of carefully constructed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">supporting hyperplanes and halfspaces</i> . The algorithm is numerically demonstrated to converge on 138 test cases with up the 3375 buses to feasible high-quality solutions (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> AC feasibility restoration (i.e., using LP solvers exclusively), (ii) in computation times generally within the same order of magnitude as those from a state-of-the-art NLP solver, and (iii) with robustness against the choice of starting point. In particular, the (relative) optimality gaps and the mean constraint violations are on average around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula> % and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-7}$</tex-math></inline-formula> , respectively, under a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> parameter setting for all the 138 test cases. To the best of our knowledge, the proposed SLP approach is the first to use LP exclusively to reach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feasible</i> and high-quality solutions to the nonconvex AC OPF in a reliable way, which paves the way for system and market operators to keep using their LP solvers but now with the ability to accurately capture transmission losses, price reactive power (Q-LMP), and obtain more accurate LMP.

THE ONLINE MARKETING COMMUNICATION IN REAL-TIME MARKETING DURING COVID-19

The purpose of the article is to characterize the assumptions and benefits of real-time marketing as a way of responding to the situation caused by the coronavirus. An important goal is also to characterize the specificity of online marketing communication during the COVID-19 pandemic. It was found that the messages focus on image, brand awareness, CSR activities, and much less on selling and communicating the product directly. The content and form of messages correspond to the required restrictions - social distance, staying at home, spending time with family, remote work, preparing meals, etc.

Strategic bidding of an energy storage agent in a joint energy and reserve market under stochastic generation

This work presents a bi-level optimization model for a price-maker energy storage agent, to determine the optimal hourly offering/bidding strategies in pool-based markets, under wind power generation uncertainty. The upper-level problem aims at maximizing storage agent's expected profits, whereas at the lower-level problem, a two-stage sequential market clearing procedure takes place. The first stage refers to a jointly cleared energy and reserve day-ahead market, deriving the optimal quantities for energy dispatch and reserve procurement, while at the second stage, a real-time energy-only market settlement is realized, considering plausible wind power generation scenarios. Real-time decisions include activated upward and downward reserves, already procured in the day-ahead market. The bi-level model is initially transformed into a mathematical program with equilibrium constraints -by recasting the lower-level problem using its Karush-Kuhn-Tucker optimality conditions-, which is further reduced into a mixed integer linear program. The proposed mathematical framework is applied to a 6-bus power grid, incurred by network transmission constraints. Numerical simulations illustrate an explicit perspective of the potential arbitrage opportunities for the storage agent, when acting strategically in energy and reserve markets, under wind generation increment scenarios and plausible transmission line congestions.

Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism

Distributed energy resources and controllable load can be aggregated in virtual power plant to participate in the bidding of day-ahead electricity market, intraday demand response market, regulation market, real-time electricity market and carbon trading market. However, the uncertainty output of distributed energy generation brings higher transaction risk to virtual power plant. Given this background, an operation model including wind turbine, electric vehicle, gas turbine and controllable load are constructed in virtual power plant. Through further analysis the carbon-electricity integrated market characteristics, a carbon-electricity integrated optimal bidding strategy of the virtual power plant is constructed, and the uncertainty of renewable energy power generation, load and market price is considered. Some assumptions are made, and a joint bidding strategy model for a VPP participating in bidding of the carbon-electricity integrated market is presented based on the robust optimization theory. Then, the commercial solver CPLEX 12.2 is next used to solve the developed robust optimization model. Finally, a virtual power plant demonstration area is used as the examples to demonstrate the feasibility and efficiency of the developed model and algorithm. The result shows that the electric vehicles and demand response transaction can effectively stabilize output fluctuation of renewable energy power generation, promote the utilization of renewable energy and improve the economic benefits of virtual power plant. Introducing carbon trading mechanism can significantly reduce the bidding output of high-carbon generation unit, and then affect the energy output structure and bidding strategy in virtual power plant.

Strategic bidding of a multi‐carrier microgrid in energy market

Microgrid can contribute to the day-ahead market by submitting bids to minimize its costs. The bidding problem is challenging due to various uncertainties. Thus, the present study provides a comprehensive optimal bidding strategy to determine the optimal power bids of a multi-carrier microgrid despite the interdependency of power and gas market prices on the day-ahead and real-time markets. In this work, the stochastic energy bidding in the proposed multi-carrier microgrid is solved via a two-stage procedure to benefit from day-ahead and real-time markets. In the first stage, the operator provides hourly energy bids to the distribution system operator regardless of uncertainty. Then, in the second stage, taking into account the confirmed day-ahead bids, the multi-carrier microgrid operator serves to balance the load in the real-time market stemmed from various uncertain parameters. This problem is solved as mixed-integer linear programming by CPLEX of the GAMS solver. A scenario reduction method is also employed to burden the complexity of the problem. Numerical results show the usefulness of the proposed model.

Застосування інструментів цифрового маркетингу для підвищення ефективності підприємницької діяльності

Abstract. Introduction. The advantages of digital marketing include better audience coverage, the ability to collect clear and detailed data, flexible approach, interactivity, lack of territorial restrictions in the implementation of marketing ideas, easy access to resources, real-time marketing management. Purpose. The purpose of the study is to substantiate and develop theoretical and practical principles of using digital marketing tools to improve business efficiency. Results. Based on the analysis of the definitions of "digital marketing", our own definition is proposed: it is the marketing of goods and services through digital channels and tools in virtual and real environments for effective interaction with existing and potential consumers. The main trends in digital marketing include the growing popularity of video content, social networks and messengers such as promotion channels, multi-channel marketing, working with "big data", mobile content and more. Based on the review of trends in educational content and requests of the generation of "millennials" for information, it was found that the medical center "Gold Clinic" should develop its digital information platforms in the direction of filling with relevant content, providing different formats of content, providing it in real time time. Conclusions. Gold Clinic lags behind in terms of number of views, number of pages viewed and time spent on the site, as well as in terms of visibility on the Internet. Therefore, now content marketing and search engine optimization should become strategic priorities to improve the efficiency of digital marketing of the enterprise. Thus, in order to personalize the content for clients of the Gold Clinic, it is advisable to use modern technologies such as artificial intelligence, machine learning, decisionmaking based on the analysis of "big data", augmented reality and more.

Two-stage interval scheduling of virtual power plant in day-ahead and real-time markets considering compressed air energy storage wind turbine

Air pollution, global warming, depletion of fossil fuels, and difficulty of transferring power to remote areas are some factors that have led to the growth of distributed generation. In addition, low capacity, invisibility to the independent system operator, and uncertainty in their output power has led to the scheduling of these units as a virtual power plant (VPP). In this study, a VPP including wind turbines (WT), photo-voltaic (PV) panels, conventional generator (CG), energy storage systems, and controllable loads is studied in a day-ahead and real-time market framework. Uncertainties in WT output power, PV output power, load, and market prices are modeled using the interval uncertainty method. In addition, demand response program is modeled using utility function. A new dispatchable WT, i.e. compressed air energy storage WT (CA-WT) technology, is evaluated in terms of its impact on VPP profit and uncertainty management. In order to evaluate the the benefit of investing in a CA-WT as compared to a traditional WT, two scenarios are considered. The scheduling problem is modeled as a mixed-integer linear program using GAMS and solved by CPLEX. Simulation is performed on a 33-bus distribution network. The results show that CA-WT effectively handles uncertainty, reduces the use of CG, and increases power sales to the markets. CA-WT increased the profit of the VPP by about 19.3 and 20.2 % respectivelu in both day-ahead and real-time market. Sensitivity analysis of VPP profit on different intervals of uncertain parameters is also investigated.

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.

A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators

The electrification of large-scale bus depots will bring challenges for bus depot operators (BDOs) aiming to minimize operational costs. A potential solution for BDOs managing electric buses (EBs) with large batteries that are capable of vehicle-to-grid (V2G) operational functionalities, along with on-site generation facilities, is to actively participate in the energy market. This paper proposed a novel two-stage multi-objective stochastic optimization-based energy management system (EMS) for BDOs to trade energy in a day-ahead (DA) energy market while considering the expected cost of energy imbalances in the real-time (RT) market. The optimization model for charge–discharge scheduling is based on a mixed-integer linear programming approach. The uncertainties associated with the DA forecast are complemented by a stochastic model. Minimizing the cost of electricity and the penalty costs for battery capacity degradation are the objectives that need to be met simultaneously. The proposed EMS is evaluated using simulation studies considering a hypothetical bus depot in Australia. The effectiveness of the proposed EMS for overall financial savings is validated by comparing its performance with state-of-the-art scheduling strategies. Results show that the net energy cost for the proposed EMS is 38% lower compared to a deterministic scheduling strategy that does not allow V2G flexibilities, 33% lower compared to a deterministic scheduling strategy that allows V2G flexibilities and 17% lower compared to a stochastic scheduling strategy that minimizes overall costs.

Wind power bidding coordinated with energy storage system operation in real-time electricity market: A maximum entropy deep reinforcement learning approach

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

Economic-environmental analysis of renewable-based microgrid under a CVaR-based two-stage stochastic model with efficient integration of plug-in electric vehicle and demand response

• Developing the electric vehicles and demand response in the MG’ operation. • Analyzing the economic-environmental performance of flexible MG. • Developing a risk-aversion strategy in the MG performance with multiple technologies. • Applying the CVaR technique to manage the risk of the proposed probabilistic model. The optimal scheduling of microgrid systems as a promising milieu to improve energy efficiency and environmental benefits is faced with several challenges related to strong uncertainties. Hence, control the risk in the daily operation of the microgrid to overcome the effects of random variables such as renewable power, load, and energy prices in the proposed scheduling are urgently required. Motivated by mentioned challenges, this paper focuses on the optimal risk assessment of the microgrid incorporated with an electric vehicle parking lot, demand response program (as two emerging flexible resources), and high penetration of renewable energy (wind and solar energies) to enhance financial and environmental goals. To indicate the taken strategies (risk-taker or risk-aversion strategies) by the system operator in the daily scheduling in facing PV and wind power variation, real-time power market, load variation, and behavior of the electric vehicle drivers, the conditional value-at-risk as a risk measure criterion is employed. The proposed model is formulated as a mixed-integer linear two-stage stochastic and modeled in General Algebra Modeling System. A 33-bus smart microgrid test system is considered to demonstrate the model's effectiveness, and simulation results are presented for several cases. Results indicate that the simultaneous integration of electric vehicles and demand response in the microgrid reduces the total operation cost by 9.97%. Besides, the emission pollution and renewable power curtailment are respectively reduced up to 12.34% and 8.49% under the proposed approach. Also, under the risk-aversion strategy, the low expected operation cost is obtained in the higher risk level.