A SMP-Based Load Shifting Optimization Model for Voluntary Demand Response in Industrial Complexes

Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém—PA, Teresina—PI, Cuiabá—MT, Florianópolis—SC and São Paulo—SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina—PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.

This research paper presents a linear programming model for network flow optimization, addressing the challenge of freshwater management in Bangalore. The model highlights the practicality of linear programming in real-world scenarios. Specifically, the efficient allocation and distribution of freshwater resources from various sources, including reservoirs, rivers, and groundwater, to meet the growing demands of domestic, industrial, and agricultural sectors, while adhering to sustainable practices.

In industrial sector, certain demands that are associated with operations of pumps are accompanied by reservoirs with storage capabilities for chemicals or solvents. Consequently, such industries possess inherent demand flexibility that can be utilized. This work proposes the coordinated control of industrial pump loads for participation in Singapore’s Demand Response (DR) programme by adjusting the electrical demand for revenue and power grid benefits. A novel Transactive Energy based energy management system (EMS) is proposed for coordinated operation scheduling of such industrial pumps to become responsive to electricity market prices. The optimal scheduling problem here is formulated based on detailed mathematical model of a water distribution system and a constrained linear programming based control technique is proposed. Case studies are performed in MATLAB environment using Singapore’s electricity market data and proposed EMS controlling the water distribution system. Results show that the proposed EMS is effective in shifting electrical demand away from peak price periods according to real Singapore market price signals while maintaining operational constraints, which leads to significant reduction in electricity costs. Further, in case of DR activations, significant demand may be reduced during DR target periods and revenue from the DR market participation is found to be lucrative.

The trend towards a decentralized, decarbonized, and digital energy system is gaining momentum. A key driver of this change is the rapid penetration increase of Distributed Energy Resources (DER). Commercial consumers can offer significant contributions to future energy systems, especially by engaging in demand response services. Virtual Power Plants (VPP) can aggregate and operate DERs to provide the required energy to the local grid and allowing for the participation in wholesale energy markets. This work considers both the technical constraints of the distribution system as well as the commercial consumer's comfort preferences. A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. A case study on the IEEE 119-bus test system is carried out. Results from the case study show that the TVPP provides optimal DER scheduling, improved system reliability and increase in demand response engagement, while maintaining commercial consumer comfort levels. In addition, the profit of the TVPP increases by 49.23% relative to the baseline scenario.

Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

The demand response potential in copper production

We design a generic framework to integrate distributed simulation and optimization models. Many problems require the integration of these two types of models. For example, stochastic programming can use simulation models as a scenario generator for optimization models; in some other cases, simulation models need optimization models to help determine system parameters. The framework is shown to be able to provide various services to help the integration of simulation and optimization models. We illustrate our implementation with a product-mix example. The example integrates a discrete event simulation of a product-mix problem with a linear programming (optimization) model of such a system. The simulation updates the parameters in the optimization model, which as a result will generate a new production plan.

We design a generic framework to integrate distributed simulation and optimization models. Many problems require the integration of these two types of models. For example, stochastic programming can use simulation models as a scenario generator for optimization models; in some other cases, simulation models need optimization models to help determine system parameters. The framework is shown to be able to provide various services to help the integration of simulation and optimization models. We illustrate our implementation with a product-mix example. The example integrates a discrete event simulation of a product-mix problem with a linear programming (optimization) model of such a system. The simulation updates the parameters in the optimization model, which as a result will generate a new production plan.

In the context of demand response (DR), formulating rational electricity pricing (EP) and electricity pricing subsidy (EPS) strategies is crucial for the power grid when dealing with a high electricity user (EU), particularly an electrolytic aluminum enterprise (EAE) in an industrial park (IP). In addition, it is difficult to assess the response effectiveness of EU. This paper proposes a method to assess demand response willingness (DRW) by introducing indicators such as demand response economy and demand response potential, while taking into account carbon emission deviation. Then, the EPS is formulated based on the result of the DRW assessment. Second, this paper establishes a two-layer electricity supplier (ES)-EAE game model, in which the ES operates as the leader and EAE operates as the follower. The model takes into account the fluctuation and deviation of loads, constructs utility functions for both the leader and follower, selects dynamic EP scenarios at different time scales, and employs a large-scale global optimization particle swarm algorithm based on cooperative evolution for solving. Finally, the model's effectiveness is validated under three electricity pricing strategies: peak-valley pricing, critical peak pricing (CPP), and real-time pricing (RTP). According to the result of simulations, under the RTP strategy, the DRW of EAE has increased by 12.5% compared to the CPP strategy, and the DR load has increased by 82%. Additionally, there has been some reduction in costs of electricity consumption. This indicates that the ES can effectively guide the EU to reduce peak loads through EP, and the EU can also achieve a reasonable reduction in electricity costs.

End-use efficiency, demand response and coupling of different energy vectors are important aspects of future renewable energy systems. Growth in the number of data centres is leading to an increase in electricity demand and the emergence of a new electricity-intensive industry. Studies on data centres and energy use have so far focused mainly on energy efficiency. This paper contributes with an assessment of the potential for energy system integration of data centres via demand response and waste heat utilization, and with a review of EU policies relevant to this. Waste heat utilization is mainly an option for data centres that are close to district heating systems. Flexible electricity demand can be achieved through temporal and spatial scheduling of data centre operations. This could provide more than 10 GW of demand response in the European electricity system in 2030. Most data centres also have auxiliary power systems employing batteries and stand-by diesel generators, which could potentially be used in power system balancing. These potentials have received little attention so far and have not yet been considered in policies concerning energy or data centres. Policies are needed to capture the potential societal benefits of energy system integration of data centres. In the EU, such policies are in their nascent phase and mainly focused on energy efficiency through the voluntary Code of Conduct and criteria under the EU Ecodesign Directive. Some research and development in the field of energy efficiency and integration is also supported through the EU Horizon 2020 programme. Our analysis shows that there is considerable potential for demand response and energy system integration. This motivates greater efforts in developing future policies, policy coordination, and changes in regulation, taxation and electricity market design.

CO2 mitigating effects by waste heat utilization from industry sector to metropolitan areas

CO 2 mitigating effects by waste heat utilization from industry sector to metropolitan areas

The increasing popularity of cloud computing would require Data Centers (DCs) to be scaled up rapidly, as needed, to provide adequate computing and storage infrastructure with rapidly growing demands. Since energy costs would be crucially important for these DCs, various approaches have been proposed to operate them efficiently. Virtual data centers (VDCs) are a promising approach for this as they can efficiently provide computing and storage resources to users over a shared physical infrastructure. In the context of VDC services, this paper focuses on improving the energy efficiency of DCs by applying a Dynamic Frequency Scaling (DFS) mechanism to provision VDC services. Specifically, the frequencies applied in each hardware are adaptively adjusted as per the given service requirements. This is done to minimize the overall energy consumption in the DC hardware when embedding a specific VDC. To the best of our knowledge, this is the first work that incorporates this DFS mechanism in the VDC provisioning problem. To minimize the overall energy consumption, we develop both an integer linear programming (ILP) optimization model and efficient heuristic algorithms. Extensive simulations are conducted to show that incorporating the DFS mechanism in VDC service provisioning significantly improves the energy efficiency of a DC when compared with a scheme where this mechanism is not applied. The proposed heuristic algorithms are also efficient and perform almost as well as the optimum ILP model.

California utilities have been exploring the use of critical peak prices (CPP) to help reduce needle peaks in customer end-use loads. CPP is a form of price-responsive demand response (DR). Recent experience has shown that customers have limited knowledge of how to operate their facilities in order to reduce their electricity costs under CPP (Quantum 2004). While the lack of knowledge about how to develop and implement DR control strategies is a barrier to participation in DR programs like CPP, another barrier is the lack of automation of DR systems. During 2003 and 2004, the PIER Demand Response Research Center (DRRC) conducted a series of tests of fully automated electric demand response (Auto-DR) at 18 facilities. Overall, the average of the site-specific average coincident demand reductions was 8% from a variety of building types and facilities. Many electricity customers have suggested that automation will help them institutionalize their electric demand savings and improve their overall response and DR repeatability. This report focuses on and discusses the specific results of the Automated Critical Peak Pricing (Auto-CPP, a specific type of Auto-DR) tests that took place during 2005, which build on the automated demand response (Auto-DR) research conducted through PIER and the DRRC in 2003 and 2004. The long-term goal of this project is to understand the technical opportunities of automating demand response and to remove technical and market impediments to large-scale implementation of automated demand response (Auto-DR) in buildings and industry. A second goal of this research is to understand and identify best practices for DR strategies and opportunities. The specific objectives of the Automated Critical Peak Pricing test were as follows: (1) Demonstrate how an automated notification system for critical peak pricing can be used in large commercial facilities for demand response (DR). (2) Evaluate effectiveness of such a system. (3) Determine how customers will respond to this form of automation for CPP. (4) Evaluate what type of DR shifting and shedding strategies can be automated. (5) Explore how automation of control strategies can increase participation rates and DR saving levels with CPP. (6) Identify optimal demand response control strategies. (7) Determine occupant and tenant response.

Demand response (DR) increases the flexibility and reliability of the electricity grid as use of intermittent renewable energy sources increases. HVAC and envelope DR measures present the largest aggregate energy and peak demand savings potential of all commercial building end uses because their net demand savings occur during critical peak demand periods. Controllable envelope measures include switchable electrochromic windows, operable window attachments such as outdoor louvers, roller shades, and awnings, as well as other innovative facade technologies that can modulate both solar heat gain and daylight admission over a broad solar-optical range. This study evaluated the technical potential of DR-enabled dynamic windows to reduce critical peak demand for a prototypical medium office building situated in all 16 U.S. climates. Model predictive control (MPC) algorithms were designed to minimize electricity cost in daylit perimeter office zones through control of an electrochromic window with and without HVAC thermostat setpoint control. Conventional and time-of-use rates were used to shape the degree of DR. Median annual peak demand savings with window and thermostat control across all climate zones were 24.3 kW (4.4 W/m2) per building or 15.9 W/m2 for non-north perimeter zones. Resource adequacy at the whole building level was estimated to be 13.1 to 43.4 $/kW per year over the 30-year life of the installation. Co-benefits were increased energy efficiency, and reduced electricity cost and emissions. Visual and thermal comfort requirements were met at all times. Dynamic facades controlled by MPC have substantial technical potential for DR across all U.S. climates and warrant serious consideration for inclusion in DR portfolios.