5. Energy Storage for DemandSide Resources
Energy storage plays a crucial role in optimizing demandside resources. Demandside resources play a vital role in the energy sector as they offer important benefits for both consumers and grid operators. These resources refer to the ability of consumers to actively manage their energy consumption patterns and contribute to grid stability. By incorporating energy storage technologies, demandside resources can become even more valuable ^{[31]}. Storage allows consumers to shift their energy usage to times when electricity is cheaper or more readily available, reducing costs and optimizing energy efficiency. Additionally, energy storage enables consumers to store excess electricity generated from renewable sources and use it during highdemand periods, thereby enhancing grid reliability and reducing dependence on fossil fuels. This integration of energy storage with demandside resources presents numerous opportunities for a more sustainable and resilient energy system in the future. In a study by ^{[32]}, the authors proposed a framework where neighboring microgrids form a multimicrogrid (multiMG) to install a shared Cloud Energy Storage (CES) with the aim of increasing profit and improving reliability. They went further to evaluate different investment scenarios, considering the yearly reward from Transmission System Operators (TSO) and Distribution System Operators (DSO), and provided a decision table for selecting the best investment scenario. This research contributes to providing a practical plan to make CES investment affordable for microgrids and considering multiple criteria in the decisionmaking process.
The authors in ^{[33]} presented a stochastic model and a novel mixedinteger model to determine the optimal capacity and operation of Largescale Energy Storage Systems (LESS) in coordination with a demand response scheme. The proposed model considers managerial options, such as the number of allowed demand response actions and the number of allowed charging and discharging. From the work, the simulation results demonstrated the significant economic impacts of considering these options. This research aimed at enhancing the economic viability of demand response programs by integrating largescale energy storage systems into the distribution substation.
Community Energy Storage (CES) is an effective tool for congestion management and operational cost optimization in distribution systems, thus, providing economic benefits to both the microgrids and the (DSO). The significance of CES in tackling congestion and costrelated concerns in distribution systems is emphasized in ^{[34]}, and the demonstration of the effectiveness of CES in lowering energy prices for the microgrids and controlling congestion in the distribution system is studied. The work employed a TeachingLearningBased Optimization (TLBO) algorithm to optimize the operation of the battery within microgrids, resulting in a decrease in energy costs for the microgrids. Additionally, the CES system is utilized to alleviate congestion in the distribution system, with the DSO purchasing charged power from the storage manager, even at a premium price exceeding the prevailing electricity rate during congested hours. This mechanism effectively alleviates congestion and provides economic benefits to both the microgrids and the DSO. Furthermore, the authors also presented a comparative analysis of heuristic algorithms, hence, highlighting the superior performance of the TLBO algorithm in achieving cost reduction and optimizing the distribution system, leading to cost efficiency.
The impact of energy storage facilities on the flexibility of active distribution networks cannot be underestimated, as it plays a vital role in eliminating challenges related to renewable energy resources. In the work by the authors in ^{[35]}, a stochastic economical flexibility evaluation method based on the envelope of the feasible operation region to the uncertain space was proposed. The work provided insights into the impact of different uncertain sources on the flexibility quantification process while taking into account the distribution network’s operation cost. The flexibility that storage systems provide to the distribution networks is quantified economically, and sensitivity analysis is performed to evaluate how battery storage system capacity impacts the flexibility index.
Energy storage for demandside resources offers several benefits and presents certain challenges. One of the main advantages is the ability to provide a reliable and stable energy supply during peak demand periods. This helps to ensure grid stability and reduce the need for additional power generation infrastructure. Energy storage also enables demand response programs, allowing consumers to actively manage and optimize their energy usage. Additionally, it facilitates the integration of renewable energy sources by smoothing out fluctuations in supply. However, there are challenges associated with energy storage, such as high upfront costs and limited technological advancements. The challenges related to thermal storage are examined in ^{[36]}, where a state of the art of present applications of thermal storage for demandside management are examined. The effectiveness of thermal energy storage for managing the mismatch between the availability of renewable electricity and the demand for electricity in buildings where hot water, heating, and cooling are delivered by heat pumps and air cooling is investigated. Results showed a reduction of 46.7% in the peak demand and a shift of approximately 37% of the total energy consumption from the peak and shoulder period to the offpeak period.
In the future, energy storage for demandside resources is expected to undergo several trends. Firstly, there will be a shift towards more advanced battery technologies, such as lithiumion and flow batteries, which offer higher energy densities and longer lifespans. Secondly, there will be a greater emphasis on decentralized storage systems, allowing individual consumers to store and manage their own energy. This will be facilitated through the use of smart grids and advanced control systems. Additionally, the integration of renewable energy sources with energy storage will become more prevalent, enabling a more reliable and sustainable energy supply.
6. State of Art in Control and Automation in Distribution Networks
The control and automation technologies in ADNs are very important. They enhance grid stability, enable realtime decisionmaking, and optimize energy distribution. Control algorithms have evolved significantly, enabling more precise and adaptive control strategies ^{[37]}. The latest developments in control algorithms include model predictive control (MPC), distributed control, and machine learningbased approaches. The distributed control system is an essential component of an ADN, enabling efficient, reliable, and resilient grid operation in the face of increasing complexity and the integration of renewable energy sources ^{[14]}. It empowers utilities and operators to manage the dynamic nature of ADNs effectively and adapt to evolving energy landscapes, ultimately contributing to a more sustainable and efficient energy distribution system. Supervisory Control and Data Acquisition (SCADA) systems are the backbone of control and automation in ADNs. The control and automation technologies are indispensable for managing the complexities of ADNs ^{[21]}.
Active distribution networks (ADNs) enable grid operators to adapt to the changing energy landscape, integrate renewable resources, optimize grid operation, enhance grid resilience, and respond to grid events in realtime, ultimately contributing to a more reliable, efficient, and sustainable energy distribution system. This section critically evaluates the enabling technologies that facilitate control and automation in ADNs, with a primary focus on control and automation technologies themselves. The main operational goal of the distribution network is to deliver electricity of an adequate level of quality securely and reliably while ensuring economical, efficient, and environmentally sustainable operation. To facilitate the realization of this goal, the distribution network operator (DNO) makes use of a distribution management system (DMS). A DMS is essentially a set of functionalities (or applications) that facilitate the monitoring, control, and automation of distribution network operations. It acts as a decision support system, utilizing which the DNO can have a realtime overview of the state of the network and initiate automatic or manual actions meant to maintain reliable and efficient system operation or mitigate anomalous network conditions. Reliability and quality of supply are ensured primarily by minimizing the occurrence of power outages, minimizing the outage time, and maintaining the system frequency and voltage magnitude at nominal levels throughout the network ^{[38]}.
A distribution management system (DMS) can also be seen as an approach that unites primarily disparate facilities to operate the distribution system in an effective, efficient, and organized way. It has several functionalities to assist operators in managing and optimizing distribution networks through suitable decisionmaking. A DMS has two types of application functions, which are the (i) realtime application functions and (ii) analytical application functions ^{[39]}.
DMS is also required to perform a variety of on and offline analyses to implement such functionalities as fault location, isolation service restoration, and network reconfiguration. A Supervisory Control and Data Acquisition (SCADA) system is an integral component of a DMS, which enables network monitoring, automation, and control. The architecture and key functionalities of DMS are seen in Figure 2. Among the key functionalities of the DMS, the following can be identified, some of which are also depicted below.
Distribution system power flow (DSPF): The voltage phasors are computed on the nodes given the information of the grid characteristics and power dispatched, representation supply, and loads. Realtime DSPF employs forward/backward sweep for radial network and current injections for meshed distribution network ^{[40]}^{[41]}.
Volt/VAR control (VVC): This is a DMS functionality that is mainly employed in managing voltage levels and reactive power flows throughout the network. Reactive power flows have a major impact on the network voltage profile. Devices employed for VVC encompass those that directly inject reactive power into the grid (e.g., shunt capacitors and shunt reactors), and those that directly control voltage (e.g., load tapchanging (LTC) transformers and voltage regulators). Traditionally, LTCs, voltage regulators, and capacitor banks have been the primary devices used in implementing VVC. With the continued gradual increase in the amount of distributed energy resource technologies integrated into the distribution network, however, these are also anticipated to take part in VVC. Smart inverters, for example, can inject reactive power into the grid and thus constitute a good alternative VVC resource ^{[42]}^{[43]}. The VVC functionality facilitates the mitigation against excessively low or high voltages, which may not only impact the power quality but might additionally pose a threat to the safety of electrical equipment. Besides voltage regulation, VVC also plays an important role in minimizing system losses and relieving key network equipment (such as transformers and feeders) of excessive loading (by reducing the capacity required to satisfy a given load demand).
Load shedding management: This is a DMS functionality that enables controlled balancing of supply and demand, where the demand exceeds the available supply. The main aim is to preserve the integrity of the entire distribution network by interrupting the electricity supply to noncritical loads to prevent a supply shortfall from cascading and threatening to cause the failure of the whole system. An automated loadshedding management application can detect predetermined trigger conditions that necessitate the initiation of predefined loadshedding control actions. If nonautomated, operators have the responsibility to manually initiate the loadshedding operations, which should be done in a safe and controlled manner to always maintain normal service to as large a part of the network as possible. Trigger conditions for load shedding, whether automated or manually initiated, include underfrequency load shedding, limit violations, and timeofdaybased load shedding ^{[44]}.
Load balancing: This involves rerouting loads to other parts of the network to relieve certain distribution equipment of overload conditions. This is often achieved through network reconfiguration. A feeder load management unit in the load balancing functionality monitors the loads on the feeders and identifies possible areas of congestion that may be susceptible to overload conditions. Feeder reconfiguration may also be used for other network operation improvement measures, such as voltage profile improvement and loss reduction. Other benefits of load balancing include improved network utilization and reduced stress on key network infrastructure ^{[44]}.
Distribution system state estimation (DSSE): It uses the state vector of the distribution network for estimation, where the least square approach is used on redundant measurements ^{[41]}. The industrial sector requires premium power quality, so either current or power methods are used to achieve high computational performance ^{[45]}^{[46]}.
Optimal feeder reconfiguration (OFR): The main task of this function is to find ways to reduce generation losses by selecting the best radial system configuration. This is attained by solving an optimization that arranges the structure of the distribution network and has a load balance between feeders. The frequently used method due to the challenges of optimization is the switch exchange heuristic ^{[39]}^{[47]}^{[48]}.
Distribution system short circuit computation (DSSCC): This function evaluates the capability of circuit breakers to perform adequately under maximum current operation conditions. It further checks on relay sensitivity under minimal current faults ^{[40]}^{[49]}.
Figure 2. Architecture and key functionalities of a DMS ^{[44]}.
Fault Locator (FLOC): It employs statuses of fault locators that communicate with DMS to locate fault branches in a network and then employs terminal impedancebased method approaches to precisely localize the faulty line ^{[41]}^{[50]}. Fault Isolation (FISO): This is a method of isolating a fault by opening circuit breakers. It isolates a small portion of the distribution network by opening switches upstream and downstream of the fault ^{[51]}. Service Restoration (SRE): Dispatches power to load nodes affected due to FISO via alternative feeders ^{[48]}.
Load forecasting: This is a key component of the distribution management system that enables the system operator to assess the short, medium, and longterm expected load on the network, which then facilitates network planning and network infrastructure development. Shortterm load forecasting is considered to have a duration of up to one day, mediumterm load forecasting is considered to have a duration of up to one year, and longterm forecasting is considered to have a longer duration of up to ten years. With the growing integration of distributed generation in the distribution network, generation forecasting is also becoming a key functionality of the DMS. This involves the analysis of weather patterns, seasonality, and other data to estimate the output that can be expected from distributed generation, especially the weatherdependent intermittent renewable generation (e.g., photovoltaic and wind power generation). Effective load forecasting contributes greatly to reliable, economical, and efficient network operation ^{[52]}.
The transition from passive to active distribution networks necessitates the development of advanced distribution management system functionalities that can handle the growing complexity of distribution network operation in the presence of a variety of active distributed resources, such as distributed generation, distributed energy storage, demandside management, and demand response.
7. Optimization Techniques for DemandSide Resources in Active Distribution Networks
The integration of demandside resources (DSRs) into active distribution networks has gained significant attention in recent years due to the increasing need for more sustainable and efficient energy systems. Thus, it is important to explore the optimization techniques employed to harness the full potential of DSRs within active distribution networks, with a particular focus on the modeling of these resources. The effective utilization of DSRs can enhance grid reliability, reduce energy costs, and promote environmental sustainability.
Table 1 outlines the various optimization methods in the literature and their limitations.
Table 1. Optimization methods and limitations.
Authors

Type

Implementation

Resources

Limitations

^{[53]}

MILP (Classical)

Reduced annualized cost by optimally selecting several system components and renewables on a smart grid.

Gridtied with microgrid with solar PV, CHP, backedup boilers, and loads (Simulationbased).

Struggles to handle the optimization of multiinput and output systems.

^{[54]}

Multiobjective framework using MILP (Classical)

Avoid power export by optimizing the multiple cogeneration systems, such as combined heat and power in microgrid residential areas. An operational planning model to mimic energy loss characteristics between storage tanks and a hot water calculating model regarding energy loss on network pipes were developed using MILP. This resulted in a reduction in residential units involved in the hot water supply network.

Gridtied with microgrid residence cogeneration system, gasfired boiler, storage tank, and loads (hot water demand) (Simulationbased).

Struggles to handle the optimization of multiinput and output systems. It struggles to handle the system with high disturbances.

^{[55]}

Fuzzy logicbased decisionmaking framework (Heuristic)

Optimize power dispatched to the grid through storage systems. Maximize electricity generation through renewables and revenue to microgrid owners during timevarying electrical costs.

Gridtied microgrid with renewable energy sources, battery storage, and loads (Simulationbased).

Slow in transient and systems with a high volume of dynamics.

^{[56]}

MILP(Classical)

Investigates how the combination of electrical and thermal storage can reduce energy cost by enabling the microgrid to improve using its power produced inhouse.

Gridtied with microgrid solar PV, geothermal heat pumps, solar thermal energy plant, thermal energy storage, battery storage, and loads (Simulationbased) (Real data).

However, high investment costs made them unprofitable at the current price condition.

^{[57]}

Improved teaching learningbased optimization (Heuristic)

Minimize the impact of intermittency and fluctuation of renewables by controlling DG output power, altering network topologies, and managing demandside load.

Gridtied microgrid with wind turbines, solar PV, and loads (Simulationbased).

Inability to predict the future behavior of the system.

^{[58]}

Advanced model prediction control (Heuristic)

Maximize the high penetration of renewables in the microgrid and minimize the running cost by solving optimization problems at each sampling time while meeting the demand and accounting for technical and physical constraints.

Gridtied microgrid with battery storage, fuel cells, wind turbines, hydrogen electrolyzer, solar PV, hydrogen tanks, and loads (Simulationbased).

Scalability, complexity, and controllability challenge.

^{[59]}

MPC (Heuristic)

Minimize energy cost and maximize battery lifespan by employing a microgrid central controller to optimally choose the adequate pattern for charging and discharging.

Gridtied microgrid with energy storage, wind, and solar PV (Simulationbased).

Slow in handling fast transient systems.

^{[60]}

Fuzzy logic adaptive prediction control (Heuristic)

Tune the input parameter on a cost function from the diesel generator and fuel cell to optimally regulate frequency in the microgrid.

Standalone microgrid that is made up of fuel cells, diesel generator, wind turbine battery storage, and loads (Simulationbased).

The high number of input variables affects model formulation, leading to more computational power needed.

^{[61]}

Stochastic receding horizon control

Minimize uncertainties from renewable energy sources by employing simplified ZBus with sequential linear programming to linearize nonlinear system dynamics. The controllable DG, switchable shunt capacitor, storage unit, and onload tap changing transformer are jointly optimized to reduce cost, and constraint violations are mitigated.

Gridtied microgrid with solar PV, wind turbines, and loads (Simulationbased).

Slow in handling fast transient systems.

^{[62]}

Enhanced model predictive control (Heuristic)

Minimize consumption from the grid, improve battery lifespan, and increase renewable sources’ participation in catering for the load.

Gridtied with microgrid PV, Battery ban, and loads (Simulationbased).

Accuracy of models is a challenge.

^{[63]}

Adaptive predictive control (Heuristic)

Minimize frequency fluctuations in the existence of disturbances and mitigate oscillations caused by external disturbances on tie line variation.

Gridtied microgrid with a diesel generator, flywheel, battery storage, fuel cell, wind turbines, hydrogen electrolyzer, and loads (Simulationbased).

The model becomes complex when handling a large number of controls.

7.1. Principles and Different Methods Used in Classical Optimization
Classical optimization methods operate on the assumption that the objective function and constraints are deterministic and known with certainty. The core principle is to identify the optimal solution by systematically searching through the feasible solution space. Key elements of these methods include mathematical modeling, objective function formulation, decision variables, and constraints. The foundation of classical optimization methods lies in mathematical modeling. It is crucial to know how optimization problems related to DSRs are represented mathematically. It represents the formulation of objective functions, identification of decision variables, and incorporation of constraints. An accurate mathematical representation is critical for developing optimization models that reflect the unique characteristics of DSRs.
Linear programming is particularly wellsuited for optimizing DSRs in active distribution networks where objectives and constraints are linear ^{[64]}. LP models can be used to minimize operational costs, such as energy procurement costs or grid infrastructure investments, while adhering to constraints related to capacity, voltage limits, and DSR availability. LP provides valuable insights into cost minimization and resource allocation. The integration of discrete decisions into DSR optimization is addressed by mixedinteger linear programming ^{[65]}. MILP models are beneficial for problems that involve both continuous and discrete decision variables, which are common in DSR management ^{[66]}. Examples include determining optimal loadshedding strategies during demand response events or selecting the best time to charge electric vehicles (EVs).
Deterministic optimization methods are instrumental in load scheduling for DSRs. Classical techniques, such as linear programming and mixedinteger programming, are used to optimize the timing and magnitude of electricity consumption for various DSRs, including industrial processes, HVAC systems, and residential loads. These methods ensure efficient energy utilization while meeting operational and economic objectives. Classical optimization methods are employed in demand response (DR) programs, where the goal is to optimize DSR participation to balance supply and demand ^{[67]}. These models allow utilities to maximize the economic benefits of DSR participation while ensuring grid stability.
7.2. Heuristic/NonDeterministic Optimization Methods
Heuristic and nondeterministic optimization methods have gained prominence in optimizing demandside resources (DSRs) within active distribution networks. These methods offer valuable tools for solving complex, nonlinear, and computationally intensive DSR optimization problems ^{[68]}. Heuristic and nondeterministic optimization methods are characterized by their ability to handle complex and computationally challenging problems. Unlike classical optimization methods, they do not guarantee globally optimal solutions but provide nearoptimal solutions within a reasonable computation time. These methods are particularly wellsuited for DSR optimization in dynamic and uncertain environments ^{[69]}.
Metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, are widely used in DSR optimization ^{[70]}. Genetic algorithms mimic the process of natural selection to evolve potential solutions to optimization problems ^{[71]}. They are effective in optimizing DSRs by exploring a diverse solution space and iteratively improving solutions. Genetic Algorithms (GAS) have been applied to DSR management problems, such as load scheduling and demand response planning, often yielding nearoptimal solutions in complex scenarios. Particle Swarm Optimization (PSO) is inspired by the social behavior of birds flocking or fish schooling. It optimizes DSRs by iteratively adjusting potential solutions based on both individual and group performance. PSO has been applied in DSR optimization to tackle problems involving distributed resources, such as distributed energy generation and load control ^{[72]}.
8. Conclusions
This research provides an overview of control, automation, and optimization of demandside resources in active distribution systems. The literature survey is based on several developments in the distribution network, which is a linchpin of electric distribution power supply. The fundamental differences between passive and active distribution networks were investigated, and their differences are presented with and providing a unidirectional flow of power, which is beneficial to both the utility and the consumer in terms of financial benefits and provides more energy stability and grid resistance. The planning and design of the traditional grid were looked at, and the role of DNO, network security, and reliability with performance indicators such as LOP, LOLE, reserve margin, and EENS. The research also investigated the planning and design of ADN, which is influenced by energy markets and technology innovation.
The innovation in the information technological space, growth of DG, and deregulation of the electric power supply industry fasttracked the concept of active distribution systems. The volume of information exchange between different players in the active distribution network requires adequate protocol to standardize data models in the electricity system equipment and provide operational specifications, such as interoperability, which is why the IEC 61850 standard is implemented. The high information exchange results in vulnerabilities and attacks through attaining access to substation automation controls, such as GOOSE flooding and intrusion of SMV, which poses a major challenge in communication and data exchange. Moreover, this also affects smart metering technologies through the generation of the interfering signal and high frequencies on the communication line, but different mitigation measures are taken, such as integrated anomaly detection and data collected from simulated attacks, employing softwaredefined network (SDN) switch eliminates possible paths of intrusion and network data overload and intrusion detection system as a countermeasure.
The optimization techniques of demandside resources in and, such as the deterministic optimization methods and nondeterministic methods, were critically studied. The research concludes that IoE is becoming a major player in ADS, which assists in the reduction of faults due to human error, fast responses, and improved stability of power supply. It is also worth noting that automatization and control also have financial benefits to the consumer due to the bidirectional flow of power. In DRs, at a lowvoltage level, the challenge becomes the privacy of their information, how to claim compensation from the markets is still not vivid enough, and the accuracy of the measurements from the consumer to the ISO. Cyber threats and security breaches are also and will still be a continuous challenge that needs frequent evaluation and mitigation measures so as not to collapse the power supply system. Multiinput and output systems are a major challenge to both the classical optimization techniques and the heuristic ones. ADN is made up of complex nonlinear systems whose requirements need techniques that respond timely, effectively, and adequately to different configurations. Classical optimization, however, cannot handle such complexities. In heuristic techniques, the high frequency of change in system dynamics affects the model formation of MPC due to the complexities and requires high computational power to run such simulations. The introduction of artificial intelligence (AI) could further improve the performance of techniques, such as MPC in microgrids, and deep learning could also deal with imprecise models.