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Demand-Side Management in Smart Grids
Published in Muhammad Asif, Handbook of Energy Transitions, 2023
Intisar Ali Sajjad, Haroon Farooq, Waqas Ali, Rehan Liaqat
DSM is a type of electricity management activity that entails a set of demand-side technology, strategies, and initiatives aimed at increasing energy efficiency, lowering costs, and lowering emissions [12] There are three components to it: (i) demand response, (ii) energy storage management, and (iii) energy efficiency management. Energy efficiency management minimizes total energy usage by improving power facilities and enforcing regulatory guidelines. Through energy reserve, energy storage management may alleviate peak demand. DR focuses on shifting load using pricing schemes and other incentives. DSM can increase the efficiency of electricity products, optimize how they are used, save resources, protect the environment, and reduce total costs. Figure 12.6 depicts the DSM participants, benefits, policies, and classifications.
The Valiant Effort to Replace Fossil Fuels
Published in H. B. Glushakow, Energy Miracles, 2022
The other day I received a check in the mail for $500. My power utility company was reimbursing me for purchasing an “energy efficient” hot water heater. They were paying me for using less power. But why would they be doing this when they obviously make more money when I use more electricity? The simple answer is that they do it to prevent blackouts. Blackouts (or power outages) are the results of breakdowns in the network of electricity grids. During peak demand periods, when the electrical supply companies cannot meet the need, grids become overloaded, and blackouts occur. Such blackouts are becoming more and more frequent in the U.S., due to increased needs, the proliferation of severe weather events, and the fragile national grids that are unable to keep up with a rocketing energy demand.
Electric and Gas Utility Rates for Commercial and Industrial Customers
Published in Stephen A. Roosa, Steve Doty, Wayne C. Turner, Energy Management Handbook, 2020
R. Scott Frazier, Lynda J. White, Richard A. Wakefield, Jairo A. Gutierrez
As described above, electric utilities must be able to meet the peak demand—the period when the greatest number of customers and large loads are simultaneously demanding service. Gas utilities must also be responsive to daily or hourly peak use of gas. In either case, the utility will need to generate or purchase enough power to cover its firm customers’ needs at all times. Demand-related costs are dependent upon overall system requirements. Demand costs can be allocated in many different ways, but utilities tend to allocate on-peak load. Included in these costs are the capital and operating costs for production, transmission, equipment (e.g., transformers) and storage (in the case of gas utilities) that vary with demand requirements. Essentially, equipment must be oversized and operated for the peak demand scenarios.
Demand response of grid-connected microgrid based on metaheuristic optimization algorithm
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Arvind R. Singh, Lei Ding, D. Koteswara Raju, R. Seshu Kumar, L. Phani Raghav
The liberalization of electricity markets attracts energy policymakers and independent system operators to explore new market opportunities to gain techno-economic benefits. With this regard, a new stochastic-based microgrid energy demand management problem is formulated in this research paper. The stochastic-based scenario generation and reduction approach is adopted to address the uncertainty associated with solar and wind power output. Unlike the existing approaches, the flexible load responsive model is derived for each demand response program to characterize the sensitiveness of customers participation. The forecasted load demand is modified according to the characteristics of price-based demand response programs, namely Time-of-use, Critical Peak Pricing, Real-time Pricing, and a combination of both Time-of-use and Critical Peak Pricing. The proposed demand response programs significantly reduce peak demand by about 13.66% and a 3.1% reduction in energy consumption, especially in Critical Peak Pricing. Further, the load factor and the system’s reliability are enhanced by implementing proposed demand response programs. The proposed problem is evaluated on a three-feeder microgrid test system, and the optimal scheduling configuration in conjunction with demand response programs is attained by employing Black Widow Optimization. The proposed algorithm outperforms the existing metaheuristic algorithms in terms of computational time and convergence. Finally, the TOPSIS method is employed to choose the best demand response program based on evaluated technical and economic performance indices. The authors are planning to extend the proposed approach to a large-scale multi-microgrid environment in the presence of various flexible energy resources.
Performance enhancement of smart grid with demand side management program contemplating the effect of uncertainty of renewable energy sources
Published in Smart Science, 2023
Chitrangada Roy, Dushmanta Kumar Das
Here, load shifting-based modified DSM model has been presented [43]. The major goal of adopting DSM in a smart grid is to narrow the gap between the reference and actual load patterns in a day-ahead market. During the load shifting process of DSM, only the shiftable loads take part. It causes a shift of load demand of electricity users from the peak demand hours to the off-peak demand hours. It also causes the load curve’s profile to flatten without changing the day’s overall energy consumption. The load profile can be represented as follows once the DSM program has been used [43]:
Development of Hybrid Energy System for a Remote Area in Kutch District of Gujarat State, India
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Arushi Misra, Mahendra Pal Sharma
The main resources available in the study area are solar, wind, and biomass energy. By integrating these resources with the battery storage and DG, the entire demand of the area can be met all the time. The inclusion of battery storage helps in storing the excess energy which can later be used during peak demand. Similarly, DG, being an on-demand generator, can take care of the peak load during the night time.